Journal articles: 'Minimization of task time'

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Maqsood, Maria, Saima Anwar Lashari, Murtaja Ali Saare, Sari Ali Sari, Yaqdhan Mahmood Hussein, and Hatem Oday Hatem. "Minimization Response Time Task scheduling Algorithm." IOP Conference Series: Materials Science and Engineering 705 (December 2, 2019): 012008. http://dx.doi.org/10.1088/1757-899x/705/1/012008.

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Hemamalini, M., and M. V. "Response Time Minimization Task Scheduling Algorithm." International Journal of Computer Applications 145, no. 1 (July 15, 2016): 9–14. http://dx.doi.org/10.5120/ijca2016910532.

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Kasprowicz, T. "Methods of Allocation of Task Teams to the Planned Works." Archives of Civil Engineering 65, no. 2 (June 1, 2019): 91–105. http://dx.doi.org/10.2478/ace-2019-0021.

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Abstract Small construction objects are often built by standard task teams. The problem is, how to allocate these teams to individual works? To solve the problem of allocation three methods have been developed. The first method allows to designate optimal allocation of teams to the individual works in deterministic conditions of implementation. As a criterion of the optimal allocation can be applied: “the minimization of time” or “the minimization of costs” of works execution. The second method has been developed analogously for both criteria but for stochastic conditions and for the stochastic data. The third method allows to appoint a compromise allocation of teams. In this case, the criteria “the minimization of time” and “the minimization of costs” are considered simultaneously. The method can be applied in deterministic or stochastic conditions of works implementation. The solutions of the allocation problems which have been described allow to designate the optimal allocation of task teams and to determine the schedule and cost of works execution.

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Kumar, Arvind, and Bashir Alam. "Task Scheduling in Real Time Systems with Energy Harvesting and Energy Minimization." Journal of Computer Science 14, no. 8 (August 1, 2018): 1126–33. http://dx.doi.org/10.3844/jcssp.2018.1126.1133.

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Maravelias, Christos T., and Ignacio E. Grossmann. "Minimization of the Makespan with a Discrete-Time State−Task Network Formulation." Industrial & Engineering Chemistry Research 42, no. 24 (November 2003): 6252–57. http://dx.doi.org/10.1021/ie034053b.

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Asim, Muhammad, Wali Khan Mashwani, and Ahmed A. Abd El-Latif. "Energy and task completion time minimization algorithm for UAVs-empowered MEC SYSTEM." Sustainable Computing: Informatics and Systems 35 (September 2022): 100698. http://dx.doi.org/10.1016/j.suscom.2022.100698.

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Zhou, Junlong, Tongquan Wei, Mingsong Chen, Jianming Yan, Xiaobo Sharon Hu, and Yue Ma. "Thermal-Aware Task Scheduling for Energy Minimization in Heterogeneous Real-Time MPSoC Systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35, no. 8 (August 2016): 1269–82. http://dx.doi.org/10.1109/tcad.2015.2501286.

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Meng, Kaitao, Deshi Li, Xiaofan He, and Mingliu Liu. "Space Pruning Based Time Minimization in Delay Constrained Multi-Task UAV-Based Sensing." IEEE Transactions on Vehicular Technology 70, no. 3 (March 2021): 2836–49. http://dx.doi.org/10.1109/tvt.2021.3061243.

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Zhang, Bingxin, Guopeng Zhang, Shuai Ma, Kun Yang, and Kezhi Wang. "Efficient Multitask Scheduling for Completion Time Minimization in UAV-Assisted Mobile Edge Computing." Mobile Information Systems 2020 (June 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/8791030.

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Mobile edge computing (MEC) can alleviate the computing resource shortage problem of mobile user equipment (UEs). However, due to long communication distance or the obstruction of big obstacles, the direct communication link may not exist between a UE and a MEC node. It thus hinders the task offloading in MEC. Unmanned aerial vehicles (UAVs) have high degree of mobility and can carry lightweight computation and storage modules. This paper presents a UAV-assisted MEC method, in which the UAV can relay the task-input data of a UE to the MEC node and can also utilize the airborne computation and storage resource to shorten the execution time of the offloaded tasks. Considering the strict order dependency among multiple offloaded tasks, this paper optimizes the task scheduling and the UAV flight path in a joint manner. A heuristic algorithm based on particle swarm optimization (PSO) is also developed to find the optimal solution. The simulation results show that the proposed multitask scheduling method can always find the best tradeoff between the UAV’s position and the wireless channel condition. In comparison to the other three baseline scheduling methods, the proposed method can use the minimum execution time to complete all the offloaded tasks.

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Fazal, Nayyer, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal, and Shahbaz Khan. "Task allocation in multi-robot system using resource sharing with dynamic threshold approach." PLOS ONE 17, no. 5 (May 4, 2022): e0267982. http://dx.doi.org/10.1371/journal.pone.0267982.

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Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.

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Yingfeng Wang, Hong Tu, and Yong Yao. "A Technique of Regulating Task and Performance Mode Orders for Voltage Transition Time Minimization." International Journal of Digital Content Technology and its Applications 5, no. 11 (November 30, 2011): 252–56. http://dx.doi.org/10.4156/jdcta.vol5.issue11.32.

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Zhou, Junlong, Jianming Yan, Jing Chen, and Tongquan Wei. "Peak Temperature Minimization via Task Allocation and Splitting for Heterogeneous MPSoC Real-Time Systems." Journal of Signal Processing Systems 84, no. 1 (April 9, 2015): 111–21. http://dx.doi.org/10.1007/s11265-015-0994-4.

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Kumar, Ajitesh, and Sanjai Kumar Gupta. "Synchronization-Aware Task Allocation Techniques for Preemption Control to Reduce Blocking Time in Multiprocessor Real-Time System." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (October 2020): 60–79. http://dx.doi.org/10.4018/ijertcs.2020100104.

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Multiprocessor real-time systems receive a great deal of attention. For better utilization of multiprocessors in a real-time context, an optimal approach for scheduling, allocation, and synchronization is required. In this research, a novel heuristic synchronization-aware scheduling has been proposed to reduce the blocking delays in a critical section and also bound to minimize multiple priority inversion. The key idea of this technique is to assign the task set in the same processor that accesses a common shared resource and also access them for the longest period of time; thereby, the global sharing of resource transforms into local sharing. From simulation results, it was concluded that the duration of blocking overheads should be minimized up to 25% to 30% and context switching between processors also reduced up to 10% to 15%. On the basis of result analysis, schedulability, minimization of context switching, and reduced blocking time indicate that the proposed method outperforms the existing methods and does not affect the task completion time.

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Babaee, H., and T. P. Sapsis. "A minimization principle for the description of modes associated with finite-time instabilities." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2186 (February 2016): 20150779. http://dx.doi.org/10.1098/rspa.2015.0779.

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We introduce a minimization formulation for the determination of a finite-dimensional, time-dependent, orthonormal basis that captures directions of the phase space associated with transient instabilities. While these instabilities have finite lifetime, they can play a crucial role either by altering the system dynamics through the activation of other instabilities or by creating sudden nonlinear energy transfers that lead to extreme responses. However, their essentially transient character makes their description a particularly challenging task. We develop a minimization framework that focuses on the optimal approximation of the system dynamics in the neighbourhood of the system state. This minimization formulation results in differential equations that evolve a time-dependent basis so that it optimally approximates the most unstable directions. We demonstrate the capability of the method for two families of problems: (i) linear systems, including the advection–diffusion operator in a strongly non-normal regime as well as the Orr–Sommerfeld/Squire operator, and (ii) nonlinear problems, including a low-dimensional system with transient instabilities and the vertical jet in cross-flow. We demonstrate that the time-dependent subspace captures the strongly transient non-normal energy growth (in the short-time regime), while for longer times the modes capture the expected asymptotic behaviour.

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Mukherjee, Proshikshya, Prasant Kumar Pattnaik, Tanmaya Swain, and Amlan Datta. "Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE." Open Computer Science 9, no. 1 (October 16, 2019): 279–91. http://dx.doi.org/10.1515/comp-2019-0016.

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AbstractThis Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model in a cloud environment and also deals with minimization of the waiting time of the task. The simulated results of the algorithm give better outcomes as compared to other existing algorithms by 15 percent.

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Singh, Harikesh, and Shishir Kumar. "Analysis & Minimization of the Effect of Delay on Load Balancing for Efficient Web Server Queueing Model." International Journal of System Dynamics Applications 3, no. 4 (October 2014): 1–16. http://dx.doi.org/10.4018/ijsda.2014100101.

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Load balancing applications introduce delays due to load relocation among various web servers and depend upon the design of balancing algorithms and resources required to share in the large and wide applications. The performance of web servers depends upon the efficient sharing of the resources and it can be evaluated by the overall task completion time of the tasks based on the load balancing algorithm. Each load balancing algorithm introduces delay in the task allocation among the web servers, but still improved the performance of web servers dynamically. As a result, the queue-length of web server and average waiting time of tasks decreases with load balancing instants based on zero, deterministic, and random types of delay. In this paper, the effects of delay due to load balancing have been analyzed based on the factors: average queue-length and average waiting time of tasks. In the proposed Ratio Factor Based Delay Model (RFBDM), the above factors are minimized and improved the functioning of the web server system based on the average task completion time of each web server node. Based on the ratio of average task completion time, the average queue-length and average waiting time of the tasks allocated to the web server have been analyzed and simulated with Monte-Carlo simulation. The results of simulation have shown that the effects of delays in terms of average queue-length and average waiting time using proposed model have minimized in comparison to existing delay models of the web servers.

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Lou, Jing, and Hong Xiang Xu. "Search Technology of Resource Service in Manufacturing Grid Based on Ant Colony Optimization Algorithm." Applied Mechanics and Materials 29-32 (August 2010): 1008–15. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1008.

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In Manufacturing Grid (MG) system, there are primarily two kinds of users: resource service consumer and resource service provider. For a resource service request task, the MG system should search the services which are qualified for the requirements of consumer and then choose the optimal one to execute it. In according to the distributed system structure of MG and node routing model influenced by several factors (e.g., time minimization, cost minimization, bandwidth minimization), the searching technology based on web service technology and ant colony optimization (ACO) algorithm has been proposed in this paper. The basic evaluation models and formulations are described, and then the algorithms are presented to minimize the expenses. The experimental results also show that the proposed method is useful in solving the searching problems in MG system.

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V, Karunakaran. "A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM." Journal of Soft Computing Paradigm 2019, no. 1 (September 22, 2019): 41–48. http://dx.doi.org/10.36548/jscp.2019.1.005.

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Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

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Khan, Fayaz Ahmad. "A Sound Assessment of Test Suite Minimization Techniques." Asian Journal of Computer Science and Technology 7, no. 2 (August 5, 2018): 87–91. http://dx.doi.org/10.51983/ajcst-2018.7.2.1865.

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During software development, testing and re-testing occurs frequently to ensure that the software is working correctly before and after modifications. To carry out an effective testing process a test suite is created and executed to detect the faults in the existing code as well as in the modified code. The manual approach of test suite creation and execution is time consuming and labour intensive task as compared to automatically generated test data or test suite. The automatic test data generation is supposed to be an effective way, but a lot of redundant test cases are generated that increase the time, effort and cost of testing. Therefore, test suite minimization techniques are used to further minimize or reduce the number of test cases by selecting a subset from an initially random and large test suite to test the code before as well as after modification. In this study, a comprehensive analysis of the different test suite minimization techniques is presented in order to extend the existing studies and to propose new ideas in this direction.

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Gorobetz, Mikhail, Ivars Alps, and Anatoly Levchenkov. "Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune Systems." Scientific Journal of Riga Technical University. Power and Electrical Engineering 25, no. 25 (January 1, 2009): 159–64. http://dx.doi.org/10.2478/v10144-009-0034-y.

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Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune SystemsThis paper describes mathematical formulation and application of artificial immune system for scheduling tasks for public electric transport. Artificial immune system is inspired by human immune system to simulate the process of interaction between antigens and antibodies. The task of scheduling in transport system is represented as one of the most well-known flow shop problem. Artificial immune system as a genetic based method is used to solve such task. Mathematical model and algorithm is proposed to create optimal schedule for public electric transport for minimization of electric energy consumption and time. Numerical example shows several steps of algorithm for artificial immune system for scheduling task solution.

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Doya, Kenji. "Reinforcement Learning in Continuous Time and Space." Neural Computation 12, no. 1 (January 1, 2000): 219–45. http://dx.doi.org/10.1162/089976600300015961.

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This article presents a reinforcement learning framework for continuous-time dynamical systems without a priori discretization of time, state, and action. Basedonthe Hamilton-Jacobi-Bellman (HJB) equation for infinite-horizon, discounted reward problems, we derive algorithms for estimating value functions and improving policies with the use of function approximators. The process of value function estimation is formulated as the minimization of a continuous-time form of the temporal difference (TD) error. Update methods based on backward Euler approximation and exponential eligibility traces are derived, and their correspondences with the conventional residual gradient, TD (0), and TD (λ) algorithms are shown. For policy improvement, two methods—a continuous actor-critic method and a value-gradient-based greedy policy—are formulated. As a special case of the latter, a nonlinear feedback control law using the value gradient and the model of the input gain is derived. The advantage updating, a model-free algorithm derived previously, is also formulated in the HJB-based framework. The performance of the proposed algorithms is first tested in a nonlinear control task of swinging a pendulum up with limited torque. It is shown in the simulations that (1) the task is accomplished by the continuous actor-critic method in a number of trials several times fewer than by the conventional discrete actor-critic method; (2) among the continuous policy update methods, the value-gradient-based policy with a known or learned dynamic model performs several times better than the actor-critic method; and (3) a value function update using exponential eligibility traces is more efficient and stable than that based on Euler approximation. The algorithms are then tested in a higher-dimensional task: cart-pole swing-up. This task is accomplished in several hundred trials using the value-gradient-based policy with a learned dynamic model.

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Pande, Sohan Kumar, Sanjaya Kumar Panda, and Satyabrata Das. "A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment." International Journal of Cloud Applications and Computing 6, no. 4 (October 2016): 1–17. http://dx.doi.org/10.4018/ijcac.2016100101.

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Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

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Garg, Akanksha, Navdeep S.Sethi, Nidhi Arora, and Amit Makkar. "BNP TASK SCHEDULING ALGORITHMS FOR PERFORMANCE EVALUATION IN PARALLEL SYSTEM." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 8 (February 20, 2014): 3768–77. http://dx.doi.org/10.24297/ijct.v12i8.3014.

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Scheduling is the process to minimize the schedule length by proper allocation of the tasks to the processors and arrangement of execution sequencing of the tasks. Multiprocessor Scheduling using Directed Acyclic Graph (DAG) is used in this research. Â An important implication of minimization of schedule length is that the system throughput is maximized. The objective of this survey is to describe various scheduling algorithms and their functionalities in a contrasting fashion as well as examine their relative merits in terms of performance and time-complexity. In this research, three BNP Scheduling Algorithms are considered namely HLFET Algorithm, MCP Algorithm and ETF Algorithm to calculate effective output by comparing the algorithms with eight test case scenarios with varying number of nodes and processors.

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Jientrakul, Ranon, Pornsak Attavanish, Pholchai Chotiprayanakul, Sunpasit Limnararat, and Chumpol Yuangyai. "Variation minimization in tele-sandblasting system: the effect of human-arm movement error." MATEC Web of Conferences 192 (2018): 01052. http://dx.doi.org/10.1051/matecconf/201819201052.

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In tele-sandblasting task, human arm movement is a critical source of producing variation in position of sandblasting nozzle resulting in high operating cost and low productivity. Each operator behaves differently leading to unpredictable movements. Skilled operators are able to reduce the variation; however, developing skills requires a training period. In this paper, we proposed a new approach which is the use of a novel operator's arm movement pattern incorporated with a Kalman filter to reduce the effect of human-arm movement error. A virtual tele-sandblasting system is used to validate our approach. The experimental results verify that our proposed approach is able to significantly reduce the effect of human arm movement error. The approach helps operators to perform the task more comfortably and takes short training time.

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Zhang, Jian Jun, Yong Qu, and Dan Mei. "A Heterogeneity Based Greedy Algorithm for Scheduling Out-Tree Task Graphs." Applied Mechanics and Materials 475-476 (December 2013): 972–77. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.972.

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The scheduling of Out-Tree task graphs is one of the critical factors in implementing the compilers of parallel languages and improving the performance of parallel computing. When applied to Out-Tree task graphs, many previous classical heterogeneity based algorithms always ignored the economization on processors and the minimization of the schedule length, which led to low efficiency in real applications. This paper proposes a heterogeneity based greedy algorithm for scheduling Out-Tree task graphs, which is based on list and task duplication, tries to find the best point between balancing loads and shortening the schedule length and improves the schedule performance without increasing the time complexity of the algorithm. The comparative experimental results demonstrate that the proposed algorithm could achieve shorter schedule length while using less number of processors.

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Yuan, Lin, Sean R. Leventhal, Junjun Gu, and Gang Qu. "TALk: A Temperature-Aware Leakage Minimization Technique for Real-Time Systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 30, no. 10 (October 2011): 1564–68. http://dx.doi.org/10.1109/tcad.2011.2160541.

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Luu, Matthew, and Andrew S. Wixom. "Substructured optimization for a speakerphone design task." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A167. http://dx.doi.org/10.1121/10.0010995.

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This work explores the use of substructures and parallelized optimization techniques in order to design speakerphone casings, the same problem considered by Berggren et al . [in 10th World Congress on Structural and Multidisciplinary Optimization (2013)]. For this work, a simplified model consisting of a 1D beam model with enforced vibration at one end is used to represent the supporting structure of the speaker and microphone of the device. The thickness of the supporting structure is varied in order to reduce coupling between the speaker and microphone. Minimization of the structural vibration in the microphone region over frequencies between 300 and 3400 Hz is examined for optimization. A substructuring approach using spectral elements and Legendre polynomials for the thickness profile reduces the computation cost such that many evaluations of the model may be obtained in a reasonable time. The Python optimization library PyGMO is used to distribute optimization tasks over multiple CPUs in order to further accelerate the design process. The approach is shown to successfully optimize a thickness distribution targeting the frequencies of interest while reducing computation costs and is compared to previously published results for this example problem.

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Wang, Jia, MengChu Zhou, Xiu Jin, Xiwang Guo, Liang Qi, and Xu Wang. "Variance Minimization Hedging Analysis Based on a Time-Varying Markovian DCC-GARCH Model." IEEE Transactions on Automation Science and Engineering 17, no. 2 (April 2020): 621–32. http://dx.doi.org/10.1109/tase.2019.2938673.

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Chakravarty, Amiya K., and Avraham Shtub. "A cost minimization procedure for mixed model production lines with normally distributed task times." European Journal of Operational Research 23, no. 1 (January 1986): 25–36. http://dx.doi.org/10.1016/0377-2217(86)90211-0.

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Mai, Long, Nhu-Ngoc Dao, and Minho Park. "Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing." Sensors 18, no. 9 (August 27, 2018): 2830. http://dx.doi.org/10.3390/s18092830.

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The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

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Sajid, Mohammad, and Zahid Raza. "Turnaround Time Minimization-Based Static Scheduling Model Using Task Duplication for Fine-Grained Parallel Applications onto Hybrid Cloud Environment." IETE Journal of Research 62, no. 3 (August 19, 2015): 402–14. http://dx.doi.org/10.1080/03772063.2015.1075911.

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Yilmaz, Hamid, and Mustafa Yilmaz. "Multi-manned assembly line balancing problem with balanced load density." Assembly Automation 35, no. 1 (February 2, 2015): 137–42. http://dx.doi.org/10.1108/aa-05-2014-041.

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Purpose – The purpose of this paper is balancing multi-manned assembly lines with load-balancing constraints in addition to conventional ones Most research works about the multi-manned assembly line balancing problems are focused on the conventional industrial measures that minimize total number of workers, number of multi-manned workstations or both. Design/methodology/approach – This paper provides a remedial constraint for the model to balance task load density for each worker in workstations. Findings – Comparisons between the proposed mathematical model and the existing multi-manned mathematical model show a quite promising better task load density performance for the proposed approach. Originality/value – In this paper, a mathematical model that combines the minimization of multi-manned stations, worker numbers and difference of task load density of workers is proposed for the first time.

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Abdallah, Mohamed, HyungWon Kim, Mohammad Ragab, and Elsayed Hemayed. "Zero-Shot Deep Learning for Media Mining: Person Spotting and Face Clustering in Video Big Data." Electronics 8, no. 12 (November 22, 2019): 1394. http://dx.doi.org/10.3390/electronics8121394.

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The analysis of frame sequences in talk show videos, which is necessary for media mining and television production, requires significant manual efforts and is a very time-consuming process. Given the vast amount of unlabeled face frames from talk show videos, we address and propose a solution to the problem of recognizing and clustering faces. In this paper, we propose a TV media mining system that is based on a deep convolutional neural network approach, which has been trained with a triplet loss minimization method. The main function of the proposed system is the indexing and clustering of video data for achieving an effective media production analysis of individuals in talk show videos and rapidly identifying a specific individual in video data in real-time processing. Our system uses several face datasets from Labeled Faces in the Wild (LFW), which is a collection of unlabeled web face images, as well as YouTube Faces and talk show faces datasets. In the recognition (person spotting) task, our system achieves an F-measure of 0.996 for the collection of unlabeled web face images dataset and an F-measure of 0.972 for the talk show faces dataset. In the clustering task, our system achieves an F-measure of 0.764 and 0.935 for the YouTube Faces database and the LFW dataset, respectively, while achieving an F-measure of 0.832 for the talk show faces dataset, an improvement of 5.4%, 6.5%, and 8.2% over the previous methods.

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Grzywacz, Norberto M., and Rosario M. Balboa. "A Bayesian Framework for Sensory Adaptation." Neural Computation 14, no. 3 (March 1, 2002): 543–59. http://dx.doi.org/10.1162/089976602317250898.

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Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.

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Ramya, G., and M. Chandrasekaran. "Solving Job Shop Scheduling Problem Based on Employee Availability Constraint." Applied Mechanics and Materials 376 (August 2013): 197–206. http://dx.doi.org/10.4028/www.scientific.net/amm.376.197.

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Manufacturing System is enabled with an excellent knowledge on production plan, proper scheduling of machinery process, employee timetabling and labor costs. Heuristic algorithms are developed to bring optimized results in stipulated time with respect to optimum schedule. This article deals with minimizing the maximum completion time (makespan) based on job scheduling and minimization of labor costs based on employee workload with Shuffled Frog Leaping Algorithm and Sheep Flock Heredity Model Algorithm. The labor costs minimization and makespan which is to find a schedule that satisfies the organizations rules, employees preferences, due date and customers. The formulation of assigning workload for employees is concerned with assigning number of employees into a given set of shifts over a fixed period of time and week task. The main problem attempts to minimize labor costs based on performance criteria and assigning the loads equally among all employees. Several local search methods and heuristics algorithms has been proposed in many research on Job shop scheduling. The Results are compared with other heuristics in terms of makespan, idle time and Labor costs the Shuffled Frog Leaping algorithm performs result oriented than other Heuristics Algorithm.

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Zemliak, Alexander. "Circuit Optimization Study According to the Maximum Principle." WSEAS TRANSACTIONS ON COMPUTERS 20 (December 9, 2021): 362–71. http://dx.doi.org/10.37394/23205.2021.20.38.

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The minimization of the processor time of designing can be formulated as a problem of time minimization for transitional process of dynamic system. A special control vector that changes the internal structure of the equations of optimization procedure serves as a principal tool for searching the best strategies with the minimal CPU time. In this case a well-known maximum principle of Pontryagin is the best theoretical approach for finding of the optimum structure of control vector. Practical approach for realization of the maximum principle is based on the analysis of behavior of a Hamiltonian for various strategies of optimization. The possibility of applying the maximum principle to the problem of optimization of electronic circuits is analyzed. It is shown that in spite of the fact that the problem of optimization is formulated as a nonlinear task, and the maximum principle in this case isn't a sufficient condition for obtaining a minimum of the functional, it is possible to obtain the decision in the form of local minima. The relative acceleration of the CPU time for the best strategy found by means of maximum principle compared with the traditional approach is equal two to three orders of magnitude.

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Dobrosotskikh, M. G. "CONSIDERATION OF STOCHASTIC IMPACTS IN THE CONSTRUCTION SCHEDULING." Proceedings of the Southwest State University 22, no. 6 (March 27, 2019): 61–71. http://dx.doi.org/10.21869/2223-1560-2018-22-6-61-71.

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There is show an experience of modern methods of scheduling in construction. There are reviewed existed scheduling methods: Critical Path Method, Constraints Programming, Job Shop Scheduling. Additionally there were reviewed methods with special edition for construction industry: shortest path planning, continue development frontline volume method, continue resources utilization method. All reviewed methods are simplified and don’t consider stochastic factors. Specific of the construction operation is a especially strong influence of stochastic factors to the construction production processes. There were reviewed methods of time reserve utilization, which appears in different stages of operations. This time reserve could be used, in particular, for minimization of negative aftereffects of stochastic factor influence on elements of construction. For these purpose was created target function of negative aftereffects minimization task, which describes dynamic and stochastic loses. The contribution of stochastic factors is expressed by exponential functions. There is shown, that redistribution of time reserve allows without any dynamic loses, to decrease contribution of stochastic loses. There is shown, that in approximation of independent works, the optimal schedule is that, which considers increasing of time reserve on critical directions. There is showed on individual example of algorithm for negative factors aftereffect minimization. Using this algorithm allows to make schedule with details of minimal approximated stochastic loses. In opposite, having a possibility of resources redistribution to directions, associated by high risks and loses, the optimal schedule plan will be alternative schedule plan, considering a possibility of operative redistribution, even through risks rise on non-critical directions.

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Sun, Zhenzhen, and Yuanlong Yu. "Robust multi-class feature selection via l2,0-norm regularization minimization." Intelligent Data Analysis 26, no. 1 (January 14, 2022): 57–73. http://dx.doi.org/10.3233/ida-205724.

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Feature selection is an important data preprocessing in data mining and machine learning, that can reduce the number of features without deteriorating model’s performance. Recently, sparse regression has received considerable attention in feature selection task due to its good performance. However, because the l2,0-norm regularization term is non-convex, this problem is hard to solve, and most of the existing methods relaxed it by l2,1-norm. Unlike the existing methods, this paper proposes a novel method to solve the l2,0-norm regularized least squares problem directly based on iterative hard thresholding, which can produce exact row-sparsity solution for weights matrix, and features can be selected more precisely. Furthermore, two homotopy strategies are derived to reduce the computational time of the optimization method, which are more practical for real-world applications. The proposed method is verified on eight biological datasets, experimental results show that our method can achieve higher classification accuracy with fewer number of selected features than the approximate convex counterparts and other state-of-the-art feature selection methods.

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Lu, Hui, Zheng Zhu, Xiaoteng Wang, and Lijuan Yin. "A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/423621.

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Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP.

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Fuhrländer, Mona, and Sebastian Schöps. "Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach." Advances in Radio Science 19 (December 17, 2021): 41–48. http://dx.doi.org/10.5194/ars-19-41-2021.

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Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

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Eisenmann, Adrian, Tim Streubel, and Krzysztof Rudion. "Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm." Energies 15, no. 4 (February 17, 2022): 1492. http://dx.doi.org/10.3390/en15041492.

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In modern electrical grids, the number of nonlinear grid elements and actively controlled loads is rising. Maintaining the power quality will therefore become a challenging task. This paper presents a power quality mitigation method via smart demand-side management. The mitigation method is based on a genetic algorithm guided optimization for smart operational planning of the grid elements. The algorithm inherits the possibility to solve multiple, even competing, objectives. The objective function uses and translates the fitness functions of the genetic algorithm into a minimization or maximization problem, thus narrowing down the complexity of the addressed high cardinality optimization problem. The NSGA-II algorithm is used to obtain feasible solutions for the auto optimization of the demand-side management. A simplified industrial grid with five different machines is used as a case study to showcase the minimization of the harmonic distortion to normative limits for all time steps during a day at a specific grid node, while maintaining the productivity of the underlying industrial process.

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Tan, Wei, Yongjiang Hu, Yuefei Zhao, Wenguang Li, Yongke Li, and Xiaomeng Zhang. "Heterogeneous Multi UAV Mission Planning Based on Ant Colony Algorithm Powered BP Neural Network." Computational Intelligence and Neuroscience 2021 (December 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/4369201.

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With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation, this paper proposes a heterogeneous multi UAV task planning technology based on ant colony algorithm powered BP neural network. The planning, research, and design are mainly carried out according to the actual situation of the UAV flight test, and the mathematical programming model is established according to the UAV load degree and maximum flight distance as constraints. This paper focuses on the contribution of the ant colony optimization algorithm to benefit maximization and task minimization. The experimental results show that the BP neural network optimized by the ant colony algorithm can improve the number of iterations and training time. Compared with some comparative algorithms, its performance is better.

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Mehrabi, Mahshid, Shiwei Shen, Yilun Hai, Vincent Latzko, George Koudouridis, Xavier Gelabert, Martin Reisslein, and Frank Fitzek. "Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks." Network 1, no. 2 (September 4, 2021): 191–214. http://dx.doi.org/10.3390/network1020012.

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Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.

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Tu, Youpeng, Haiming Chen, Linjie Yan, and Xinyan Zhou. "Task Offloading Based on LSTM Prediction and Deep Reinforcement Learning for Efficient Edge Computing in IoT." Future Internet 14, no. 2 (January 18, 2022): 30. http://dx.doi.org/10.3390/fi14020030.

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In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks is proposed to solve the above task offloading decision problem. In the training phase of the model, this algorithm predicts the load of the edge server in real-time with the LSTM algorithm, which effectively improves the convergence accuracy and convergence speed of the DRL algorithm in the offloading process. In the testing phase, the LSTM network is used to predict the characteristics of the next task, and then the computational resources are allocated for the task in advance by the DRL decision model, thus further reducing the response delay of the task and enhancing the offloading performance of the system. The experimental evaluation shows that this algorithm can effectively reduce the average latency by 6.25%, the offloading cost by 25.6%, and the task throw rate by 31.7%.

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Yu, Yao, Jinxian Weng, and Wanying Zhu. "Optimizing Strategies for the Urban Work Zone with Time Window Constraints." Sustainability 11, no. 15 (August 5, 2019): 4218. http://dx.doi.org/10.3390/su11154218.

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Work zones that move with road maintenance tasks are enclosing and have caused severe traffic jams and the significant decline of road capacity. This paper proposes an intelligent-based multi-objects road maintenance optimization strategy based on a practical origin–destination (OD) matrix and complicated work schedules over a real urban road network. It focuses on the optimization of multi short-term maintenance tasks and the minimization of average travel delay for vehicles passing through. By taking the driving characteristic into account, static and dynamic variable speed limit strategies provide access to ensure safety on the working road network. Through this view, the problem was formulated as a mixed multi-object nonlinear program (MNLP) model with respect to the time window of the related sub-maintenance task. By using actual OD distribution matrix data, a series of microscopic simulated cases were conducted to test the model’s validity. Moreover, sensitive analyses of types of parameters (e.g., traffic safety threshold, traffic flow and working efficiency) with an optimal solution were discussed considering five different scenarios.

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Finta, Lucian, and Zhen Liu. "Complexity of Task Graph Scheduling with Fixed Communication Capacity." International Journal of Foundations of Computer Science 08, no. 01 (March 1997): 43–66. http://dx.doi.org/10.1142/s0129054197000045.

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Consider a scheduling problem of parallel computations in multiprocessor systems. Let a parallel program be modeled by a task graph, where vertices represent tasks and arcs the communications between tasks. An interprocessor communication time incurs when two tasks assigned to two different processors have to communicate. Such a scheduling problem has recently been studied in the literature, mostly for the case where interprocessor communication times are fully determined. In this paper, we consider the scheduling problem with communication resource constraints. More specifically, we consider the case where all interprocessor communications take place on a network of bounded capacity. We consider two variants of the problem: communications with independent-data semantics and common-data semantics. We show that even for very specific subproblems, viz. scheduling of general graphs on two processors and scheduling of binary trees on an infinite number of processors, the minimization of the makespan of parallel programs in such a multiprocessor system is strongly [Formula: see text]-hard. We first establish the results for the case of capacity 1, referred to as the single-bus system. We then extend the results to the more general case of fixed communication capacities. As a consequence, the general scheduling problem of parallel programs with communication resource constraints is strongly [Formula: see text]-hard. These results are to be contrasted with the corresponding scheduling problems without contraint on the communication capacity, where the two-processor case has unknown time complexity and the infinite-processor case is polynomial. Our results are also extended to the case of broadcasting communications, and can be applied to multiprocessor systems with shared memory.

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Sudha, R., G. Indirani, and S. Selvamuthukumaran. "Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine." Journal of Computational and Theoretical Nanoscience 18, no. 4 (April 1, 2021): 1275–81. http://dx.doi.org/10.1166/jctn.2021.9401.

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Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

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Engin, Orhan, and Batuhan Engin. "Hybrid flow shop with multiprocessor task scheduling based on earliness and tardiness penalties." Journal of Enterprise Information Management 31, no. 6 (October 8, 2018): 925–36. http://dx.doi.org/10.1108/jeim-04-2017-0051.

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Purpose Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems. Design/methodology/approach First, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3. Findings Computational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window. Originality/value There is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

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Liu, Fagui, Zhenxi Huang, and Liangming Wang. "Energy-Efficient Collaborative Task Computation Offloading in Cloud-Assisted Edge Computing for IoT Sensors." Sensors 19, no. 5 (March 4, 2019): 1105. http://dx.doi.org/10.3390/s19051105.

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As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.

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Wu, Chunyi, Gaochao Xu, Jia Zhao, and Yan Ding. "A novel large-scale task processing approach for big data across multi-domain." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401881495. http://dx.doi.org/10.1177/1687814018814955.

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Large-scale task processing for big data based on cloud computing has become a research hotspot nowadays. Many traditional task processing approaches in single domain based on cloud computing have been presented successively. Unfortunately, it is limited to some extent due to the type, price, and storage location of substrate resource. Based on this argument, a large-scale task processing approach for big data in multi-domain has been proposed in this work. While the serious problem of overheads in computation and data transmission still exists in task processing across multi-domain, to overcome this problem, a virtual network mapping algorithm based on multi-objective particle swarm optimization in multi-domain is proposed. Based on Pareto dominance theory, a fast non-dominated selection method for the optimal virtual network mapping scheme set is presented and crowding degree comparison method is employed for the final optimal mapping scheme, which contributes to the load balancing and minimization of bandwidth resource cost in data transmission. Cauchy mutation is introduced to accelerate convergence of the algorithm. Eventually, the large-scale tasks are processed efficiently. Experimental results show that the proposed approach can effectively reduce the additional consumption of computing and bandwidth resources, and greatly decrease the task processing time.

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Journal articles on the topic 'Minimization of task time'

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