Unoptimized Case Research Articles

Infrared Image Human Target Detection Method Based on Deep Learning

Introduction: In this study, we investigate human target detection techniques for infrared target recognition in complicated backdrops using deep learning approaches. This study uses the human target detection of infrared images in various scenes as its research object. Method: A test was conducted using MatlabR2010a and the real-time C language development platform. The findings indicate that, in the unoptimized case, the computing speed of the method was only 0.34 seconds. However, following optimization, its performance significantly increased to meet the real-time performance requirements. Result: The research findings presented in this work will be crucial to the relief and rescue efforts following engineering mishaps. Conclusion: This study is innovative in that it develops a deep learning-based model for human target detection in infrared photos and thoroughly examines and improves its functionality.

Operation optimization of propane pre-cooled mixed refrigerant LNG Process: A novel integration of knowledge-based and constrained Bayesian optimization approaches

Liquefied natural gas (LNG) technology, particularly the propane precooled mixed refrigerant (C3MR) process, has demonstrated efficiency and emerged as a distinctive dual-refrigerant technology widely used in LNG production. However, the liquefaction process is the highest energy-intensive stage within its supply chain as it consumes about 8 % of the LNG energy content. Thus, for the first time, this study proposes systematic knowledge-based and constrained Bayesian optimization approaches to identify the optimal operation of the C3MR process. These approaches optimize both the operational parameters (pressures and flow rates) and the composition of the mixed refrigerant with practical equipment specifications and rigorous constraints. The results show that the specific energy consumption (SEC) is reduced to 0.264 kWh/kgLNG, which is 14.6 %, and 26 % lower than the basic C3MR process (unoptimized case) and typical industrial C3MR processes, respectively. In addition, the optimized SEC in this study is 14.5 % to 38.6 % lower than those reported in the literature. At large-scale LNG production (10,000 tons per day), the reduction in the SEC is translated into an 18 MW decrease in compression power, saving approximately 4.7 million $ per year for each C3MR train. Moreover, the coefficient of performance (COP) of the C3MR process was improved by about 15 %, and the CO2 emissions were reduced by 17 % (7 tons per year) compared to the basic C3MR process, indicating potential advancements in large-scale LNG liquefaction processes.

Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm

In this study, a phased operation optimization method for active distribution network with energy storage system is proposed for the operation optimization problem of active distribution network. The proposed model considers the PV and storage system output, the number of regulation equipment action, network loss and node voltage deviation for multi-objective optimization of the active distribution network. To achieve multi-objective optimization of the proposed optimal model, this study proposes a multi-objective coati optimization algorithm based on integrated crowding distance ranking, Bernoulli chaotic mapping, external archiving and non-dominated ranking mechanism. The proposed algorithm exhibits good performance as tested by ZDT and UF test functions, and obtaining optimal values in 75 % of the results. In the test of the improved IEEE30 and IEEE33 node system, the proposed method is improved by 21.55 %, 56.10 % and 4.12 % in the three aspects of network loss, node voltage deviation and minimum voltage of the whole network compared with the unoptimized case. Therefore, the proposed method in this study has a good application value for the optimization of active distribution network operation and can support the grid-connected operation of distributed new energy sources.

Harmonics mitigated multi-objective energy optimization in PV integrated rural distribution network using modified TLBO algorithm

This paper presents an optimal operation of PV integrated distribution network to address the multiple issues such as harmonics, voltage variation, poor power factor, and power losses. A real time rural distribution network is modeled on ETAP (industrial software) to obtain un-optimized parameters and component settings, and, then the DHPF is embedded in TLBO algorithm to obtain the optimal parameters and component settings considering various constraints such as node voltage, conductor thermal loading (ampacity), transformer taps and inverter harmonic generation limits. The optimal parameters so obtained are validated again on real time distribution network and assessed in comparative reference context with the un-optimized base case. The results show that TLBO gives the best operation of the distribution network, particularly flat voltage profile, improved power factor, reduced power losses and mitigated harmonics.

Optimal design of piezoelectric cantilever velocity sensor based on PVDF

The response charge of piezoelectric speed sensors using a conventional rectangular cantilever is low, which also causes a low sensitivity in speed measurement. To improve the sensor sensitivity, a piezoelectric speed sensor based on a streamlined piezoelectric cantilever is employed in this paper. Furthermore, a theoretical optimization model of the sensor based on Bernstein polynomial equation is established, and a simulation optimization flow work is also proposed. With method of moving asymptotes (MMA) algorithm, more charge output can be obtained than before. The simulation results show that the optimized sensor can output a voltage of 416 mV and obtain a sensitivity of 52 mV/m⋅s−1 when the input speed is 8 m/s. As compared with the values of 300 mV and 37.5 mV/m⋅s−1 in the un-optimized case, the improvement in the sensor sensitivity is up to 38%, which confirms the effectiveness of the proposed method.

Optimized critical parameters for n-alkanes up to C100 for reliable multiphase behavior of hydrocarbon mixture using SRK EOS

The Soave-Redlich-Kwong equation of state (SRK EOS) is widely used for phase behavior simulation of hydrocarbon mixtures. However, its applicability and reliability are limited for heavier hydrocarbon mixtures and reservoir fluids. This research presents optimized critical temperatures (TC), critical pressures (PC), and acentric factors (ω) parameters for n-alkanes up to C100 for improved volumetric and compositional multiphase behavior predictions.Parameters TC, PC, and ω for C7 to C100 are optimized using Particle Swarm Optimization (PSO) to match the density as well as vapor pressure data. Average absolute relative deviations (AARDs) achieved are 1.9% and 2.4% respectively for density and vapor pressure matches, corresponding values for the unoptimized case are 53.23%, and 3.90% respectively. The case of optimization of ω only to match vapor pressure has AARD of 2.0% in comparison to 3.90% from unoptimized values.Binary interaction parameters (BIPs) are optimized by matching upper critical end point (UCEP) for CO2, CH4, C2, and C3 binaries with n-alkanes with their optimized TC PC ω, optimized ω (and unoptimized TC and PC) only, and unoptimized TC PC ω values cases. It is found that optimized TC PC ω is correctly able to show the presence of three-phases for all 23 ternary hydrocarbon mixtures.

Raman transitions driven by phase-modulated light in a cavity atom interferometer

Atom interferometers in optical cavities benefit from strong laser intensities and high-quality wavefronts. The laser frequency pairs that are needed for driving Raman transitions (often generated by phase modulating a monochromatic beam) form multiple standing waves in the cavity, resulting in a periodic spatial variation of the properties of the atom-light interaction along the cavity axis. Here, we model this spatial dependence and calculate two-photon Rabi frequencies and ac Stark shifts. We compare the model to measurements performed with varying cavity and pulse parameters such as cavity offset from the carrier frequency and the longitudinal position of the atom cloud. We show how setting cavity parameters to optimal values can increase the Raman transition efficiency at all positions in the cavity and nearly double the contrast in a Mach-Zehnder cavity atom interferometer in comparison to the unoptimized case.

Enabling Large-Scale Simulations of Quantum Transport with Manycore Computing

The non-equilibrium Green’s function (NEGF) is being utilized in the field of nanoscience to predict transport behaviors of electronic devices. This work explores how much performance improvement can be driven for quantum transport simulations with the aid of manycore computing, where the core numerical operation involves a recursive process of matrix multiplication. Major techniques adopted for performance enhancement are data restructuring, matrix tiling, thread scheduling, and offload computing, and we present technical details on how they are applied to optimize the performance of simulations in computing hardware, including Intel Xeon Phi Knights Landing (KNL) systems and NVIDIA general purpose graphic processing unit (GPU) devices. With a target structure of a silicon nanowire that consists of 100,000 atoms and is described with an atomistic tight-binding model, the effects of optimization techniques on the performance of simulations are rigorously tested in a KNL node equipped with two Quadro GV100 GPU devices, and we observe that computation is accelerated by a factor of up to ∼20 against the unoptimized case. The feasibility of handling large-scale workloads in a huge computing environment is also examined with nanowire simulations in a wide energy range, where good scalability is procured up to 2048 KNL nodes.

Designing a MapReduce performance model in distributed heterogeneous platforms based on benchmarking approach

MapReduce framework is an effective method for big data parallel processing. Enhancing the performance of MapReduce clusters, along with reducing their job execution time, is a fundamental challenge to this approach. In fact, one is faced with two challenges here: how to maximize the execution overlap between jobs and how to create an optimum job scheduling. Accordingly, one of the most critical challenges to achieving these goals is developing a precise model to estimate the job execution time due to the large number and high volume of the submitted jobs, limited consumable resources, and the need for proper Hadoop configuration. This paper presents a model based on MapReduce phases for predicting the execution time of jobs in a heterogeneous cluster. Moreover, a novel heuristic method is designed, which significantly reduces the makespan of the jobs. In this method, first by providing the job profiling tool, we obtain the execution details of the MapReduce phases through log analysis. Then, using machine learning methods and statistical analysis, we propose a relevant model to predict runtime. Finally, another tool called job submission and monitoring tool is used for calculating makespan. Different experiments were conducted on the benchmarks under identical conditions for all jobs. The results show that the average makespan speedup for the proposed method was higher than an unoptimized case.

Numerical investigation into the effect of compressor and expander valve timings on the performance of an Ericsson engine equipped with a gas-to-gas heat exchanger

The present investigation is a continuation of a previous study conducted by the authors that presented a dynamic-simulation model of an Ericsson engine equipped with a gas-to-gas heat exchanger. This model is here used to investigate the effect of compressor and expander valve timing variation. The investigation begins with an un-optimized case and examines various improvements in terms of valve timings. This results to an optimized case that improves power and thermal efficiency. The optimized case serves as reference to study the effects of variations in the valve timings of the compressor and expander. The inlet valve closing timing of the expander (IVCe), which determines the intake duration of the expander, necessitates the readjustment of some of the other valve timings, since it significantly affects the pressure levels in all devices. The IVCe variation led to the potential of using a single heater with multiple compressor-expander pairs that are sequentially and periodically coupled to the heater. This increases the specific power output compared to using several compressor-heater-expander triplets, since it reduces the total volume of the engine. The number of compressor-expander pairs that can be coupled to a single heater is largely determined by the IVCe timing.

Effect of turbine resistance and positioning on performance of Aerofoil wing building augmented wind energy generation

This paper provides further investigation into usefulness of Aerofoil wings to enhance wind turbines energy generation integration into buildings (Architectural forms AF). It provides insight of wind follows around and beneath Aerofoils enhancing building augmented turbines, while simulating turbines as a resistance to wind flow under wings. It builds on previous work investigating factors for enhancing Aerofoil wings integration into buildings for in-site energy generation. Wind turbine simulated as a porous jump in CFD ANSYS FLUENT. Energy generated estimated as a function of pressure drop, resulting wind velocity and area per square metre. It was found that Turbine resistance has lowered wind acceleration, but the optimized case (i.e. optimized in the previous work) maintained the highest of achieved acceleration of wind speeds scoring 13.2 m/s compared with the un-optimized case 10.85 m/s. Optimized wing cases secured more pressure drop across the turbine porous surface in all the range of studied incident velocities compared to the un-optimized cases. That is 1.6–1.9 folds more energy estimated for optimized wing over un-optimized. With proper positioning of turbine an increase up to 3.38 folds secured when turbine is positioned (2 m behind the wing tip opposite windward). The turbine should be placed 2 m from the tip for the highest yield.

An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field

The heliostat field of solar power tower (SPT) system occupies a large proportion of both the total investment and total energy losses of a plant. However, the optimization design of a heliostat field is a challenging work, because there are too many parameters to be optimized. In this paper, a new high-dimensional genetic algorithm toolbox (HDGA) is developed in Visual Studio Community 2015 for the heliostat field design, in which a new crossover strategy is employed for the high-dimensional optimization. The algorithm is verified by both mathematical models and engineering cases, the results show that HDGA is more effective for the high-dimensional problem, and its convergence speed is much faster than that of the genetic algorithm toolbox developed by the University of Sheffield (Sheffield GA). The new algorithm is explained in detail and the optimal field layout is presented. With the new algorithm, a heliostat field referencing to the Gemasolar plant is optimized in this paper. The results show that the optical performance of the heliostat field is improved significantly than that of the un-optimized case, and the optical efficiency of 63.7% is reachable at the design point. At the same time, the annual insolation weighted efficiency is 56.9%.

Optimum design of composite conical tanks under hydrostatic pressure

Elevated tanks are used all over the world to store water for times of shortage. These tanks can be made of steel, reinforced concrete, or composite, that is, concrete and steel. Composite tanks consist of an external steel shell attached to an internal reinforced concrete wall through steel studs. Composite conical tanks combine the advantages of reinforced concrete and steel tanks as they resist efficiently both tensile and compressive stresses. A comparison showed that the material cost of composite conical tanks is significantly less than that of steel or reinforced concrete tanks having the same layout dimensions. A numerical tool is developed to obtain the optimum design of composite conical tanks under hydrostatic pressure incorporating both finite element and genetic algorithm techniques. This tool is used to obtain the optimum design of a case study composite conical tank that was recently constructed. The developed optimization tool provides the thicknesses of the concrete and steel walls as well as the stud configuration corresponding to the minimum material cost. A comparison between the optimized and unoptimized case study composite tank revealed that a reduction of 32% in the material cost can be achieved. A sensitivity analysis is conducted by changing the price of concrete, steel plate, and studs by (±) 50% of the datum prices and obtaining the corresponding optimum design variables. This analysis showed that the optimum thicknesses of the concrete wall and steel shell as well as studs’ configuration are significantly sensitive to the change in the material prices.

Development of a mathematical model for optimizing a heliostat field layout using differential evolution method

Summary In this study, differential evolution was employed to perform optimization of a heliostat field. A complete mathematical code was developed for this purpose, which generates a heliostat field and calculates the optimum spacing between heliostats through differential evolution optimization technique. The optimization was executed for two sets of two cases and compared with an un-optimized case. In the first case, only the optical performance was optimized, whereas in the second case, the normalized ratio of the optical performance to the land area covered by the heliostat field was maximized. In the first set of cases, the extra security distance between the heliostats was neglected, whereas in the second set of cases, the extra security distance was taken into account. To apply and examine the application of the optimization algorithm developed, 3 days of the year were selected: March 21, June 21, and December 21, considering Dhahran, Saudi Arabia as an illustrative example. For June 21, when the extra security distance between the heliostats is neglected, the optical efficiency of the un-optimized case was 0.6026, while for the first optimized case, it was 0.6395, and for the second optimized case, it was 0.6033. However, when the extra security distance was considered, the optical efficiency of the un-optimized case was 0.6167; while for the first optimized case, it was 0.6241, and for the second optimized case, it was 0.6167. Similar observations were realized for the other cases selected. Copyright © 2015 John Wiley & Sons, Ltd.

Flux balancing of light and nutrients in a biofilm photobioreactor for maximizing photosynthetic productivity

This article reports a combined experimental and numerical study on the efficient operation of Porous Substrate Bioreactors. A comprehensive model integrating light transport, mass transport, and algal growth kinetics was used to understand the productivity of photosynthetic biofilms in response to delivery rates of photons and nutrients. The reactor under consideration was an evaporation driven Porous Substrate Bioreactor (PSBR) cultivating the cyanobacteria Anabaena variabilis as a biofilm on a porous substrate which delivers water and nutrients to the cells. In an unoptimized experimental case, this reactor was operated with a photosynthetic efficiency of 2.3%, competitive with conventional photobioreactors. Moreover, through a scaling analysis, the location at which the phosphate delivery rate decreased the growth rate to half of its uninhibited value was predicted as a function of microorganism and bioreactor properties. The numerical model along with the flux balancing techniques presented herein can serve as tools for designing and selecting operating parameters of biofilm based cultivation systems for maximum productivity.

Reduction of Torque-Induced Bending Vibrations in Ball Screw-Driven Machines via Optimal Design of the Nut

As a result of the push for sustainable machine designs, efforts are constantly being made to reduce the mass/inertia of moving machine components so as to minimize material usage and energy consumption. However, the reduction of structural stiffness that often accompanies such efforts gives rise to unwanted vibrations which must be effectively mitigated to ensure satisfactory performance of the designed machine. The ball screw mechanism (BSM) is commonly used in machines for motion and force transmission. Recent research has shown that, due to the coupling introduced by the nut, a torque applied to the shaft of a ball screw mechanism causes undesirable lateral (bending) vibrations of the screw, which adversely affect the fatigue life and positioning accuracy of ball screw-driven machines. In this paper, an analysis of the stiffness matrix connecting the screw to the nut is used to show that the entry/exit angle of the balls and the lead angle of the screw have the greatest influence on the coupling between the torsional and lateral directions. An objective function is proposed to minimize the static coupling between the applied torque and lateral deformations of the screw. The existence of local minima in the objective function is shown to be dependent on the cyclical characteristics of cross-coupling terms in the screw-nut interface stiffness matrix as a function of the entry/exit angle of the balls. Moreover, the sensitivity of the local minima to other nut/screw parameters is shown to highly depend on the lead angle. Simulations conducted on the finite element (FE) model of a single-axis ball screw-driven machine demonstrate that the optimally selected entry/exit angles result in a significant reduction of the low-frequency torque-induced vibrations of the machine compared to the unoptimized case, particularly when the lead angle is small. The proposed method is therefore suitable for reducing the torque-induced lateral vibrations of ball screws without increasing the diameter (i.e., inertia) of the screw, thus leading to more sustainable designs of ball screw-driven machines.

Optimization of gas pressures ratio in a fast-axial-flow CO2 laser with genetic algorithm

In this paper, the laser output power of a fast-axial flow CO2 laser was optimized with gas pressures ratio of CO2:N2:He using a genetic algorithm technique. The power of laser was increased from 500W (un-optimized case) to 2200W (simulated case), also experimentally the power has achieved the value of 700W (optimized case).

Optimization of a 60° waveguide bend in InP-based 2D planar photonic crystals

We present a novel design for a W1 (one missing row of holes) waveguide 60° bend implemented in a substrate-type InP/InGaAsP/InP planar photonic crystal based on a triangular array of air holes. The bend has been designed to provide high transmission over a large bandwidth. The investigated design improvement relies only on displacing holes while avoiding changing individual holes diameter in the interest of better process control (homogenous hole depth). Two-dimensional (2D) finite-element simulations were used to increase the relative transmission bandwidth from 18% to 40% of the photonic bandgap for unoptimized and optimized 60° bends, respectively. The 2D results were verified by means of rigorous three-dimensional (3D) finite-difference time-domain (FDTD) simulations. We show that excellent agreement between 2D and 3D simulations can be obtained, provided a small effective-index shift of −0.024(−0.74%) and an imaginary loss parameter (ϵ″=0.014) is introduced in the 2D simulations. To demonstrate the applicability of our improved design, the bend was fabricated and measured using the endfire technique. A bending loss of 3 dB is obtained for the optimized W1 waveguide bend compared to more than 8 dB in the unoptimized case.

Exploring the limits of leakage power reduction in caches

If current technology scaling trends hold, leakage power dissipation will soon become the dominant source of power consumption. Caches, because of the fact that they account for the largest fraction of on-chip transistors in most modern processors, are a primary candidate for attacking the leakage problem. While there has been a flurry of research in this area over the last several years, a major question remains unanswered---What is the total potential of existing architectural and circuit techniques to address this important design concern? In this paper, we explore the limits in which existing circuit and architecture technologies may address this growing problem. We first formally propose a parameterized model that can determine the optimal leakage savings based on the perfect knowledge of the address trace. By carefully applying the sleep and drowsy modes, we find that the total leakage power from the L1 instruction cache, data cache, and a unified L2 cache may be reduced to mere 3.6, 0.9, and 2.3%, respectively, of the unoptimized case. We further study how such a model can be extended to obtain the optimal leakage power savings for different cache configurations.

Handling irreducible loops

This paper addresses the question of how to handle irreducible regions during optimization, which has become even more relevant for contemporary processors since recent VLIW-like architectures highly rely on instruction scheduling. The contributions of this paper are twofold. First, a method of optimized node splitting to transform irreducible regions of control flow into reducible regions is formally defined and its correctness is shown. This method is superior to approaches previously published since it reduces the number of replicated nodes by comparison. Second, three methods that handle regions of irreducible control flow are evaluated with respect to their impact on compiler optimizations. First, traditional node splitting is evaluated. Second, optimized node splitting is implemented. Third, DJ-Graphs are utilized to recognize nesting of irreducible (and reducible) loops and apply common loop optimizations extended for irreducible loops. Experiments compare the performance of these approaches with unrecognized irreducible loops that cannot be subject to loop optimizations, which is typical for contemporary compilers. Measurements show improvements of 1 to 40% for these methods of handling irreducible loops over the unoptimized case. Optimized node splitting may be chosen to retrofit existing compilers since it has the advantage that it only requires few changes to an optimizing compiler while limiting the code growth of compiled programs compared to traditional node splitting. Recognizing loops via DJ-Graphs should be chosen for new compiler developments since it requires more changes to the optimizer but does not significantly change the code size of compiled programs while yielding comparable improvements. Handling irreducible loops should even yield more benefits for exploiting instruction-level parallelism of modern architectures in the context of global instruction scheduling and optimization techniques that may introduce irreducible loops, such as enhanced modulo scheduling.