Efficient Constraint Research Articles

Power management in co-phase traction power supply system with super capacitor energy storage for electrified railways

Increasing railway traffic and energy utilization issues prompt electrified railway systems to be more economical, efficient and sustainable. As regenerative braking energy in railway systems has huge potential for optimized utilization, a lot of research has been focusing on how to use the energy efficiently and gain sustainable benefits. The energy storage system is an alternative because it not only deals with regenerative braking energy but also smooths drastic fluctuation of load power profile and optimizes energy management. In this work, we propose a co-phase traction power supply system with super capacitor (CSS_SC) for the purpose of realizing the function of energy management and power quality management in electrified railways. Besides, the coordinated control strategy is presented to match four working modes, including traction, regenerative braking, peak shaving and valley filling. A corresponding simulation model is built in MATLAB/Simulink to verify the feasibility of the proposed system under dynamic working conditions. The results demonstrate that CSS_SC is flexible to deal with four different working conditions and can realize energy saving within the allowable voltage unbalance of 0.008% in simulation in contrast to 1.3% of the standard limit. With such a control strategy, the performance of super capacitor is controlled to comply with efficiency and safety constraints. Finally, a case study demonstrates the improvement in power fluctuation with the valley-to-peak ratio reduced by 20.3% and the daily load factor increased by 17.9%.

Mathematical modeling and optimization of spiral drum screen in the concrete residue recovery system

To overcome the drawbacks of the traditional concrete residue recovery system, the processing capacity is insufficient and the volume is large. This paper deals with the mathematical modeling and optimization of the spiral drum screen in the concrete residual recycling system. First, based on the maximum amount of material taken by the first spiral at the feed inlet, the paper establishes a theoretical model of screening capacity. Second, according to the relationship between the number of material drops and the screening efficiency, the model of screening efficiency was established from the optimal separation angle of materials. Finally, with the minimum volume of the spiral drum screening sieve as the optimization target, the optimization model was established with the constraints of system screening capacity, screening efficiency, drive power and structural strength. Based on the optimization results, spiral drum screening was produced. The experimental results show that under the condition of 90% screening efficiency, the quality of the spiral drum screen is reduced by 35%, and the screening treatment capacity is doubled, which illustrates the feasibility of the method.

Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a constraint programming (CP) model, among which four MILP models are formulated based on four different modeling ideas. MILP models are effective in solving small-scaled problems to optimality. DFJSP is NP-hard, therefore, we propose an efficient constraint programming (CP) model based on interval decision variables and domain filtering algorithms. Numerical experiments are conducted to evaluate the performance of the proposed MILP models and CP model. The results show that the sequence-based MILP model is the most efficient one, and the proposed CP model is effective in finding good quality solutions for the both the small-sized and large-sized instances. The CP model incomparably outperforms the state-of-the-art algorithms and obtains new best solutions for 11 benchmark problems. Moreover, the best MILP model and CP model have proved the optimality of 62 best-known solutions.

Design and Optimization Method for Hypersonic Quasi-Waverider

The waverider has an extensive application prospect in the design of hypersonic vehicles due to its excellent aerodynamic efficiency. However, it is known that the original cone-derived waverider is longitudinally unstable. To solve the problem, a design method for hypersonic quasi-waverider configuration is first proposed and then a genetic algorithm optimization framework is constructed to obtain optimum quasi-waveriders with different constraints. During the optimization, the aerodynamic performance is evaluated by an efficient aerodynamic model that considers the impacts of strong viscous interaction effects. Results from numerical simulations show that, for the optimum quasi-waveriders without constraints, good shock wave attachment along the leading edge is achieved, and the maximum lift-to-drag ratio () is even higher than that of the original waverider. Optimized quasi-waveriders are also generated based on the constraints of volumetric efficiency and stability. The first increases and then decreases with the increase in volumetric efficiency. In addition, an interesting phenomenon is found that the is reduced almost linearly with the increase in the degree of stability at each design condition. Furthermore, a linear relationship is also constructed between the variation of with respect to degree of stability and the viscous interaction parameter .

Backscatter Then Forward: A Relaying Scheme for Batteryless IoT Networks

In this letter, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.

Functional adaptation rather than ecogeographical rules determine body-size metrics along a thermal cline with elevation in the Chinese pygmy dormouse (Typhlomys cinereus)

Phenotypic plasticity is crucial for how organisms respond to variation in their environment, affecting their diversity and distribution, especially in the light of rapid environmental change. Ecogeographical rules predict an association between specific adaptive morphological and physiological traits with cooler conditions due to higher latitude, elevation, or climate change. Such ecogeographical effects are often most evident in ancient species due to continuous selective adaptation occurring over long periods of time. Here, we use the suitably ancient Chinese pygmy dormouse (Typhlomys cinereus) to test whether body-size, appendage length and heart size vary in accordance with Bergmann's, Allen's and Hesse's rule, respectively. Based on a sample of 67 adult individuals (female, n=29; male n=38) trapped at 37 sites transcending an elevational range from 414 to 1757m, we tested for trait concordance with Bergmann's rule (body mass, length and SMI), Allen's rule (length of tail, foot, ear, snout), and Hesse's rule (wet and dry heart mass). Effects of elevation (and thus temperature lapse rate; calculated as 0.61°C per 100m) on body size, appendage length and heart size, were tested by fitting Standardized Major Axis (SMA) models. We observed substantial heterogeneity in morphometric traits allowing for the detection of ecogeographical clines. However, none conformed with Bergmann's, Allen's (except ear size), or Hesse's rule. However, our results indicate some support for Geist's rule of net primary productivity. We conclude that pervasive functional life-history adaptations in this blind, arboreal, echolocating ancient species exceeded selection for morphological energy efficiency constraints, with the notable exception of reduced ear pinnae size at colder, elevated sites. This is an important consideration for predicting how species, and populations in general, may adapt to human induced rapid environmental change, contrary to expectations of warming driving selection for smaller body-size.

Research on multi-stage iterative model solving method with resource optimization configuration

Aiming at the problem of unreasonable resource allocation in the existing coupling design iterative model based on efficiency constraint, the resource equilibrium strategy was introduced into the iterative model. In order to reduce the time cost of product development, a multi-stage iterative model with optimal resource allocation was constructed and the optimal resource allocation of each task group was obtained by solving this model. Taking the design and development process of an air purifier as an example, the validity of this model was verified. The research shows that after introducing resource equilibrium strategy into the iterative model based on efficiency constraint, the time cost of iterative mode in different stages decreases, and with the increase of the number of iteration stages, the time cost decreases first and then increases. The research results provide a theoretical basis for designers to reasonably select resource allocation mode and number of iteration stages in actual product development so as to optimize resource allocation and reduce development cost.

UK Power Networks develop the world's first market-based approach to delivering efficient network constraint management

A market-based approach to delivering efficient network constraint management as a new commercial arrangement to improve the efficiency of connection and curtailment could be applied by any distribution network operator connecting distributed energy resources and storage customers to a constrained part of a distribution network.

Financial Technology Efficiency and Credit Constraints Facing the Industrial Sector: Evidence From China

There is a close logical correlation between structural reform of the financial sector and the enhancement of financial institutions’ economic performance. In the existing research, however, there are disagreements and gaps relating to the efficiency measurement of the financial system’s input and output, as well as the impact of financial efficiency on the credit constraints facing the industrial sector. Based on China’s provincial panel data from 2008 to 2016, this paper measures and decomposes financial technology efficiency, and selects system equations to comprehensively test the internal and external transmission mechanism of financial technology efficiency. The research finds that: (1) The key to improving the efficiency of input and output of China’s financial system lies in the effective absorption and utilization of financial system reform and financial technology innovation, that is, the utilization efficiency of financial frontier technology; (2) The key to improving the utilization efficiency of financial frontier technology lies in the overall reform of the economic system, including the development of the private economy, the expansion of market opening, the acceleration of service industry development, and the promotion of technological innovation. (3) The improvement of financial technology efficiency is the key to reducing costs, de-leveraging and increasing efficiency in the industrial sector. It can effectively alleviate the credit constraints facing the industrial sector and promote its high-quality development. Therefore, it is necessary to promote the reform and opening up of the economic system with a view to promoting the efficiency of the financial system, rather than simply resorting to financial system reform and financial technology innovation.

LSTPD: Least Slack Time-Based Preemptive Deadline Constraint Scheduler for Hadoop Clusters

Big data refers to numerous forms of complex and large datasets which need distinctive computational platforms in order to be analyzed. Hadoop is one of the popular frameworks for analytics of big data. In Hadoop, a big job is split into multiple small tasks and then they are distributed to worker nodes in a parallel way using MapReduce to speed up computational processes. In this aspect, it is important how to improve throughput performance. MapReduce jobs require quick responses from the worker nodes to complete them under their deadlines. The existing scheduling schemes for Hadoop such as FIFO, fair, and capacity schedulers cannot guarantee the quick response requirement satisfying a prior deadline. Thus, Hadoop system needs to improve response time and completion time for the heterogeneous MapReduce jobs. In this paper, we propose an efficient preemptive deadline constraint scheduler based on least slack time and data locality. In order for better allocation of tasks and load balancing, we first analyze the task scheduling behaviors of the Hadoop platform. Based on that, we propose a novel preemptive approach which considers the remaining execution time of the job being executed in deciding preemption. The experimental results show that the proposed scheme significantly reduces the job execution time and queue waiting time, compared to existing schemes.

Memory constraint parallelised resource allocation and optimal scheduling using oppositional GWO for handling big data in cloud environment

In cloud computing, task scheduling is one of the challenging troubles, especially when deadline and cost are conceived. On the other hand, the key issue of task scheduling is to reach optimal allocation of user's tasks in clouds. Besides, in terms of memory space and time complexities, the processing of a huge number of tasks with sequential algorithm results in greater computational cost. Therefore, we propose an efficient memory constraint parallelised resource allocation and optimal scheduling method based on oppositional GWO for resolving the scheduling problem of big data in the cloud environment in this paper. In parallel over distributed systems, the suggested scheduling approach applies the MapReduce framework to perform scheduling. The MapReduce framework is consisted of two main processes; particularly, the task prioritisation stage (with fuzzy C-means clustering method based on memory constraint) in map phase and optimal scheduling (using oppositional grey wolf optimisation algorithm) in reduce phase. The performance of proposed methodology is analysed in terms of makespan, cost and system utilisation.

Model Predictive Control Strategy-based Voltage Sensing of Quasi Z-Source Cascaded Multi-Level PV Inverter with Distributed MPPT Algorithm

The evolving quasi Z-source cascaded multilevel inverters (QZS-CMI) need appropriate control logic for effective switching state of operation. This work proposes the model predictive control (MPC) based QZS-CMI in a PV generation system. The MPC, with its characteristic of prediction of future response and efficient constraint handling capacity, controls the output voltage and capacitor voltage. The proposed topology effectively solves the significant detrimental aspect of switching stress usually arising in a high voltage inverter system, by the increased number of levels of the inverter circuit. The MPC based control strategy exhibits a fast-dynamic response and maintains the output power quality in an off-grid application. The simulation model with experimental results helps to validate the optimal operation of the proposed control logic in seven levels QZS-CMI.

Astronomical Data Preprocessing Implementation Based on FPGA and Data Transformation Strategy for the FAST Telescope as a Giant CPS

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. CPSS extend cyber-physical systems (CPS) to include the social domain, which introduces a challenge of massive data processing. As a typically giant CPS, the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the world’s largest filled-aperture radio telescope, generates massive volume of data which poses a huge storage problem that CPSS face likewise and requires real-time data compressing to reduce data storage and movement overhead. The recently introduced Bitshuffle preprocessing algorithm is a novel approach towards exploiting spatial redundancy incorporation to improve the compression ratio with a specific compressor. However, the existing high-performance CPU-based solutions cannot satisfy the performance requirement and power budget requirement simultaneously. In the paper, we propose the implementation of this algorithm on Field Programmable Gate Array (FPGA) and present an unique data transformation strategy to turn raw FAST data in classic FITS format into another format to support huge file sizes, i.e. Hierarchical Data Format (HDF5). Evaluation results show that our implementation can achieve 3.2Gbyte/s throughput which can be equipped with LZ4 compressor to be high performance compressor. This makes Bitshuffle on FPGAs a candidate for meeting the computational and energy efficiency constraints of radio telescopes and provide reference for CPSS facing the same situation.

Secure Cloud Storage of Text and Image Files by Giving Access Control to Users

Cloud computing is extensively used but it’s not completely trustworthy. It can be in terms of protecting or securing the confidential data from malicious attacks as well as from cloud providers. Also once the data is deployed on cloud, the user has no control over it. So rather than deploying the original text file the user encrypts the file and then uploads on the cloud. When this file needs to be shared with other it can be done using Cipher text Policy Attribute Based Encryption (CP-ABE). This gives the central access to the owner and secures the data from external attacks. Besides, the cloud provider serves both as the payee of resource consumption fee, lacking the transparency to data owners. So to reduce these factors a specific encryption strategy is been used and the time for encryption is also taken into account. The files are also encrypted by the priority that are given by the user during the file uploading section. A text file of size 1GB with the CP-ABE encryption strategy requires 174 seconds whereas encryption done using AES is 473 seconds and that of RSA is 330 seconds. Hence, this dissertation work proposes methodology to secure or protect encrypted cloud storage from Economic Denial of Sustainability attacks and give an efficient time constraint approach.

Preparation and biological evaluation of BACE1 inhibitors: Leveraging trans-cyclopropyl moieties as ligand efficient conformational constraints

Inhibition of BACE1 has become an important strategy in the quest for disease modifying agents to slow the progression of Alzheimer’s disease. We previously reported the fragment-based discovery of LY2811376, the first BACE1 inhibitor reported to demonstrate robust reduction of human CSF Aβ in a Phase I clinical trial. We also reported on the discovery of LY2886721, a potent BACE1 inhibitor that reached phase 2 clinical trials. Herein we describe the preparation and structure activity relationships (SAR) of a series of BACE1 inhibitors utilizing trans-cyclopropyl moieties as conformational constraints. The design, details of the stereochemically complex organic synthesis, and biological activity of these BACE1 inhibitors is described.

Resource-Use Efficiency And Production Constraints Among Cassava Farmers in Nigeria: Insight, Linkage and Pathway

Maximum Likelihood Estimate (MLE) of efficiency in agricultural production, specifically crop sub-sector, determines the efficiency level of households in their farming activities. If accessible, farmers in developing countries like Nigeria do not use all potential technological resources, thus making inefficient decisions in their agricultural activities. This paper therefore made effort to measure the technical efficiency (TE) of cassava production, identified drivers of technical efficiency and production constraints facing cassava farmers in Nigeria. Using cross sectional farm level data from cassava farming households in Nigeria, the study employed Stochastic Frontier Analysis (SFA) in the Cobb-Douglas production function, Tobit regression, Benefit-cost ratio to achieve the objectives of the study. The calculated technical efficiency in the study was around 65.1 percent. The estimated variance parameter of the model (γ) was 0.92 which implies that about 92 per cent of the variation in cassava output was due to farmers' practices which comprise all variables. The study showed that there was gross inefficiency in the allocation of productive resources among cassava farmers in Oyo state. Most of these resources are over-utilized implying a sub-optimal utilization of resources. The study demonstrated that the key factors significantly influencing technical efficiency levels are years of production, size of land cultivated, household size, variety of cassava planted, total number of stem cutting used for planting and quantity of agrochemicals used per hectare. The allocative efficiency measurement shows that most of the resources were sub-optimally utilized. Meanwhile, high cost of inputs (such as labor) were identified as key constraints for cassava production in the study area. This study therefore suggest that economic efficiency and enterprise productivity will improve if government broadens the scope on provision of soft loans, adequate subsidized farm inputs (like agro- chemicals). This will improve increase income, food and nutrition security for the cassava farming households in Nigeria. Additionally, in order to achieve optimality in resource allocation, there is need introduce and encourage usage of technologies that can replace or reduce the use of inputs with high cost in the cassava production process. Hence, this will raise the productivity of resources, increase output and consequently improve revenue and net returns.

Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes.

This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop multiple-relay network, where the data transmission between the source and the destination is realized with the help of DF relays. Low density parity check (LDPC) codes are adopted as forward error correction (FEC) codes to encode and decode the data both at the source and relays. We consider both fixed and variable code rates depending upon the quality-of-service (QoS) provisioning such as spectral efficiency and maximum energy efficiency. Furthermore, an optimal power allocation scheme is studied for the cooperative system under the energy efficiency constraint. We present the simulation results of our proposed scheme, compared with conventional methods, which show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay, and bit error rate (BER).

An Efficient and Fast Partial Template Matching Technique Enhancement in Normalized Cross Correlation

Template matching forms the basis of many image processing algorithms and hence the computer vision algorithms. There are many existing template matching algorithms like Sum of Absolute Difference (SAD), Normalized SAD (NSAD), Correlation methods (CORR), Normalized CORR(NCORR), Sum of Squared Difference (SSD), and Normalized SSD(NSSD). In general, as image requires more memory space for storage and much time for processing. The above said methods involves much computation. In any processing, efficiency constraints include many factors, especially accuracy of the results and speed of processing. An approach to reduce the execution time is always most appreciated. As a result of this, a novel method of partial NCC (PNCC) template matching technique is proposed in this paper. A block window approach is used to reduce the number of operations and hence to speed up the processing. A comparative study between existing NCC algorithm and the proposed partial NCC, PNCC algorithm is done. It is experimented and results proves that the execution time is reduced by 8 - 47 times approximately based on the various template images for different main images in PNCC. The accuracy of the result obtained is 100%. This proposed algorithm works for various types of images. The experiment is repeated for various sizes of templates and different sizes of main image. Further improvement in the speed of execution can be achieved by implementation of the proposed algorithm using parallel processors. It may find its importance in the real time image processing.

How to get Certain Knowledge from Fallible Justification

Abstract“Real knowledge,” as I use the term, is the most highly prized form of true belief sought by an epistemic agent. This paper argues that defeasible infinitism provides a good way to characterize real knowledge and it shows how real knowledge can arise from fallible justification. Then, I argue that there are two ways of interpreting Ernest Sosa's account of real knowledge as belief that is aptly formed and capable of being fully defended.On the one hand, if beliefs are aptly formed only if they have a specific causal etiology, namely that they are efficiently caused fully or partially by virtuous characteristics of the epistemic agent, then Sosa's account falls prey to what I call the problem of the Hazard of Empirical Disconfirmation (HED). The HED problem applies to all forms of causal accounts of real knowledge and is simply that as we gain more empirical knowledge about the causal origins of our true beliefs that are the most highly prized we will discover that they do not always (or even hardly ever) satisfy the required efficient causal constraints. Bluntly put, having sufficiently good reasons for our beliefs might not require that the beliefs have the requisite efficient causal etiology.On the other hand, there is a way of interpreting Sosa's views that does not include an efficient causal prerequisite. That interpretation makes Sosa's account of real knowledge almost identical to defeasible infinitism but expressed in an alternate vocabulary. Such a view is not subject to the HED problem and it can solve the deep problem in epistemology, namely how to get epistemically certain (as opposed to psychologically certain) knowledge from fallible justification.

User rate and power optimization for HetNets under Poisson cluster process

Heterogeneous cellular networks (HetNets) consist of different tiers of base stations (BSs) to meet the ever-increasing mobile traffic demand. Due to the random deployment of BSs, Poisson point process (PPP) is often used to model the BS distribution. However, low power small cells are usually clustered around the popular areas, and PPP can not reflect such a feature. To this end, we in this paper consider base station (BS) cooperation and analyze user rate and energy efficiency of HetNets based on a Poisson cluster process (PCP). A calculable formula for the average data rate (or spectral efficiency) and its approximated closed form are derived. Based on this closed form, a power minimization solution with certain spectral efficiency constraint is proposed, and the optimal cooperation radii are derived. Furthermore, we analyze spectral efficiency under a limited number of cooperative BSs in a two-tier network. Finally, we propose a range expansion (RE) scheme and examine the impact of this scheme. The theoretical analyses are verified by simulation results.