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The feasibility of the application of image/signal processing for measuring, marking, matching, and sorting vast quantities of data derived from materials typically found in artworks is presented through four case studies. Different patterns produced by canvas weave structures, surface textures of historic photographic papers, chain line intervals in Rembrandt's printing papers, and watermark variations have been subjected to different modes of computational analysis. The art-historical implications that result from computer-generated algorithms – including dating, attribution, authenticity, and workshop practices – can be considered as "computational connoisseurship." The case studies discussed point to future areas for research. Finally, because of the need for statistically meaningful datasets of images, a practical means of recording internal paper structure is introduced. [ABSTRACT FROM AUTHOR]

The existence of specular highlights hinders high-level computer algorithms. In this paper, we propose a novel approach to remove specular highlights from a single grayscale image by regarding the problem as an image-to-image translation task between the highlight domain and the diffuse domain. We solve this problem by using the generative adversarial network framework, where a generator removes highlights and discriminator judges whether outputs of the generator are clear and high- light-free. Specular highlight removal is intractable as we should remove specular highlights while keeping as many details as possible. Considering the similarity between the highlight image and diffuse image, we adopt an attention-based submodule that generates a mask image, which we call the highlight intensity mask, to locate pixels that contain specular highlights and help the skip-connected autoencoder to remove highlights. A pixel discriminator and Structural Similarity loss are utilized to ensure that more details can be retained in the output images. For training and testing models, we build a grayscale highlight images dataset. It consists of more than a thousand sets of grayscale highlight images with ground truth. Finally, quantitative and qualitative evaluations demonstrate the effectiveness of our method than other contrast generative adversarial network methods. [ABSTRACT FROM AUTHOR]

In order to improve the design and development efficiency of colored spun knitted products, a computer simulation algorithm is proposed. Aiming at the problem that the low-frequency color information in colored spun knitted fabrics cannot be effectively separated by a single frequency filter, the approximate component of Dual-Tree Complex Wavelet is adopted to characterize, analyze and visualize the color information. Meanwhile, an image colorization algorithm is constructed by simulating the coloring law of two-component dyed fibers on the fabric surface, replacing the original color information with the target color information, and then reconstructing the fabric image via the Dual-Tree Complex Wavelet, so as to realize the computer simulation of two-component colored spun knitted fabric. On this basis, a quantitative analysis is conducted on the simulation results via the multi-dimensional texture and color similarity indexes. Then, the significance test is carried out for the evaluation results. As indicated in the results, compared with the traditional frequency domain filtering method and the wavelet transform method, the computer simulation algorithm established in this paper can realize the computer simulation of multi-type "fancy color" effect of colored spun knitted fabrics. The simulation results of this scheme improve 4.57% and 1.87% in texture similarity, 10.23% and 6.01% in color similarity, 3.31 dB and 5.69 dB in PSNR, and 0.0161 and 0.0108 in SSIM, respectively. As shown in the significance test results, the difference significance level of the evaluation results is less than 0.05. Therefore, the simulation effect of this method is significantly improved compared with the two comparison methods. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one by Kemal Akkaya and colleagues on sensor-actor network in which sensors and another by Keqin Li on multihop wireless networks.

At the heart of this paper is an examination of the colloquial concept of a 'shadow ban'. It reveals ways in which algorithms on the Facebook platform have the effect of suppressing content distribution without specifically targeting it for removal, and examines the consequential stifling of users' speech. It reveals how the Facebook shadow ban is implemented by blocking dissemination of content in News Feed. The decision-making criteria are based on 'behaviour', a term that relates to activity of the page that is identifiable through patterns in the data. It's a technique that is rooted in computer security, and raises questions about the balance between security and freedom of expression. The paper is situated in the field of responsibility of online platforms for content moderation. It studies the experience of the shadow ban on 20 UK-based Facebook Pages over the period from November 2019 to January 2021. The potential harm was evaluated using human rights standards and a comparative metric produced from Facebook Insights data. The empirical research is connected to recent legislative developments: the EU's Digital Services Act and the UK's Online Safety Bill. Its most salient contribution may be around 'behaviour' monitoring and its interpretation by legislators. [ABSTRACT FROM AUTHOR]

Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four data-sets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size. [ABSTRACT FROM AUTHOR]

This paper aims to present a literature review and an analysis of research works in the field of economic lot scheduling problem (ELSP) based on the related articles published since 1958. Because of ELSP complexity, there are a noticeable number of studies that use algorithms based on different approaches in order to deliver a feasible solution. Therefore, the contribution of this paper is to introduce a taxonomic classification based on scheduling policies and solving methodologies proposed by authors. Also, a simple data analysis is carried out to understand the evolution of ELSP and to identify potential research areas for further studies. The results show that there is an increasing trend in this topic but there are still much needs from industrial manufacturing systems. This study is expected to provide a comprehensive list of references for other researchers, who are interested in ELSP research. [ABSTRACT FROM AUTHOR]

This paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature. [ABSTRACT FROM AUTHOR]

Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled churn prediction using learner's personal attributes from learner profiles and their overall performance. The major concern with existing approaches is that the accuracy of prediction is not satisfactory as the model is built on the sample data with limited features. This paper addresses this issue by proposing a distributed iterative classifier that deploys an ensemble learning algorithm to generalize the model for predicting potential churn from personal attributes. Predictions from the base classifier are obtained using a distributed iterative classification algorithm that deploys a map-reduce framework. Iterative classification algorithm predicts signs of attrition in the learners through their online interactions. It can also process a very large network, which was lacking in the existing solution. The proposed system is evaluated using the features of five students and results are reported. Experimental results show that the proposed ensemble classifier not only improves the performance of churn prediction significantly but also runs faster than the other algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling a set of independent jobs with set-up times on a set of machines in a permutation flow shop environment. A metaheuristic known as the Migrating Birds Optimization (MBO) is adapted for the minimization of the makespan. Two versions of the algorithm are presented. The first is a basic MBO and the second introduces additional features. An extensive computational study is conducted to analyse the efficiency of the two methods on a benchmark of instances that can be found in Sistemas de Optimizacion Aplicada (http://soa.iti.es/problem-instances). The second version outperforms by far the first one. Moreover, the results that are produced by the latter show that it is suitable for solving real-world scheduling problems. [ABSTRACT FROM AUTHOR]

The assembly hierarchy for a product design determines the subassembly module formation, assembly tasks for the modules and serial-parallel material flow among these tasks. Automatic generation of the candidate assembly hierarchies by computer algorithms is a critical step to exploring potential design space for the assembly system design, and existing research on assembly sequence generation and subassembly identification has limitations in dealing with this challenge. This paper proposes to use assembly hierarchy instead of assembly sequence to generate the design space for assembly system design and optimisation. Based on liaison graphs, this paper first characterises the assembly hierarchy by developing a unique representation model to capture the hierarchical relationship among the assembly operations. A recursive algorithm is then developed to search the candidate design space and facilitate the computer implementation of assembly system configuration design. Two case studies including a real-world laptop assembly demonstrate the effectiveness of the proposed algorithm in the reduction of repetitive exploration of design space and avoidance of missing scenarios for assembly system configuration design by comparing with state-of-the-art assembly sequence generation algorithms. The method can lead to an automated tool to evaluate the manufacturability of product designs and optimise assembly system configuration design. [ABSTRACT FROM AUTHOR]

This paper presents a single instruction multiple data tabu search (SIMD-TS) algorithm for the quadratic assignment problem (QAP) with graphics hardware acceleration. The QAP is a classical combinatorial optimisation problem that is difficult to solve optimally for even small problems with over 30 items. By using graphic hardware acceleration, the developed SIMD-TS algorithm executes 20 to 45 times faster than traditional CPU code. The computational improvement is made possible by the utilisation of the parallel computing capability of a graphics processing unit (GPU). The speed and effectiveness of this algorithm are demonstrated on QAP library problems. The main contribution of this paper is a fast and effective SIMD-TS algorithm capable of producing results for large QAPs on a desktop personal computer equivalent to the results achieved with a CPU cluster. [ABSTRACT FROM AUTHOR]

To recommend useful information to users more efficiently, this paper proposes a dual-path recommendation algorithm which combines multilayer Convolutional Neural Network (CNN) and attention-enhanced long short-term memory network (Attention-LSTM). Firstly, the matrix factorisation technique is used for learning the long-term preferences of users. Secondly, a dual-path network based on CNN and LSTM is constructed to perform feature extraction on the rating matrix. The dual-path network can learn the long-term preferences of users while capturing their dynamic preferences in changing preferences. The algorithm is tested on the public dataset MovieLens-1M, and the MAE value reflects the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

This paper concerns the role of the unconscious in technology. The central thesis is that there exists an experience of non-acceptance and failed incorporation of technology, which (a) does not depend on the technical engineering dimension of the artifact but (b) instead concerns the relationship between the human unconscious and the artifact. This thesis is supported and developed through the analysis of a case study – that is, the creation and development of the first version of Google Glass. The failure of the first version of Google Glass is explained in terms of the non-acceptance of this technology. This paper presents an analysis of users' experiences and comments as a basis for interpreting the relationship between the unconscious and technology. Their non-acceptance of this technology is explained and further clarified from a postphenomenological point of view, utilizing the concept of "technological uncanny." The analysis suggests that antimediation can be understood as a form of noise. In fact, as non-acceptance, antimediation directly concerns the relationship between contingency and control. [ABSTRACT FROM AUTHOR]

A major part of warehouse operations is related to the collection of parts from the warehouse which is called the Order Picking Problem. To improve order picking operations, the total travel distance and generally picking time must be reduced. In this paper, a two-level approach is proposed that determines the locations of parts in the warehouse. The first step clusters parts into part families. Four different clustering methods based on principal component analysis, singular value decomposition and Two-Step Cluster Component are applied. In the second step, four different heuristics are proposed to determine the locations of parts. In addition to the minimisation of travel distance, we also consider the minimisation of the total congestion in aisles due to multiple workers. The proposed algorithms also consider the interactions between part families to minimise intergroup movements. As a result of the implementation, we achieved more than 40% reduction in material handling compared to the current set-up of the warehouse. The applied algorithms can easily be modified to be used for warehouses with different configurations. The algorithms utilised in this case study can be helpful to researchers to become familiar with new heuristics, as well as practitioners to design improved warehouses. [ABSTRACT FROM AUTHOR]

Self-storage warehousing is a rapidly growing industry where consumers or companies can rent storage space for personal or company use, over a certain horizon. This paper addresses the question of how to design the facilities so that revenue can be maximised over a finite horizon, considering multiple demand requirements with respect to size, pricing, location within the facility, climate control, security or outside access, among others. Using a customer choice model to specify the probability of purchase for each fare product as a function of the set of fare products offered, we propose methods to design self-storage warehouses while allowing different customer choice behaviours. We model the problem as a mixed-integer program and solve it using column generation and branch-and-price algorithms. In addition, we study the impact of re-layout and methods to modify facility layout, since self-storage facilities are relatively flexible in layout and individual storage compartments can be adapted to changes in demand. We validate our model using the data of four self-storage warehouses, and show our method can improve the expected revenue by nearly 11% on average for these cases. [ABSTRACT FROM AUTHOR]

Connected vehicle communication technology is rapidly developing in recent years, and host vehicle (HV) can send or receive the basic safety message (BSM) from the remote vehicles (RVs). However, there are few applications using this information to improve the driving safety. In this paper, we propose a collision warning predicted framework that provides connected automated vehicle and alert driver when time to collision (TTC) is within specified thresholds. After pre-processor RV BSM data, this paper transforms the RV position and calculates the relative position, distance and speed. Then, the RV trajectory is estimated by using Kalman filter algorithm, and the error statistics of the prediction of latitude and longitude are analysed. The prediction results show that it works on straight, corner and the curve road, but the latitude error is higher than the longitude error. At last this paper constructs the relative position radar map for the HV, in which it can show the relative position, speed and TTC information. Vehicle collision can be detected in real time and the vehicle can prevent the potential conflict accordingly by using these information. [ABSTRACT FROM PUBLISHER]

Breath and cardiac sounds are two major bio sound signals. In this, heart sounds are produced by movement of some body parts such as heart valve, leaflets and the blood flow through the vessels, whereas lung sounds generates due to the air in and out flow through airways during breathing cycle. These two signals are recorded from chest region. These two signals have very high clinical importance for the patient who is critically ill. The lung functions and the cardiac cycles are continuously monitored for such patients with the help of the bio sound signal captured using suitable sensing mechanism or with auscultation techniques. But these two signals mostly superimpose with each other, so the separation of these heart sound signals (HSS) and the lung sound signals (LSS) is of great research interest. There are so many different techniques proposed for this purpose. In this paper, a study is carried out on different algorithms used for the separation of HSS from LSS, and also the results of major four separation algorithms are compared. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to develop a new approach for wiring networks diagnosis. This approach is based on time domain reflectometry (TDR) and pattern recognition network all combined with a simple localisation and characterisation algorithm. The developed approach has the advantage of being fast, reliable and accurate. The TDR responses are generated using the analytical transmission line method. Furthermore, the manipulated data are reduced since only the main peaks are exploited. The performance of the proposed approach has been assessed using four cases, two using a simple Y-shaped network and two using a more complicated YY-shaped network. The obtained results are very satisfactory, and they show the applicability of the proposed approach for real systems. [ABSTRACT FROM AUTHOR]

The class of mixed-integer quadratically constrained quadratic programs (QCQP) consists of minimizing a quadratic function under quadratic constraints where the variables could be integer or continuous. On a previous paper we introduced a method called MIQCR for solving QCQPs with the following restriction: all quadratic sub-functions of purely continuous variables are already convex. In this paper, we propose an extension of MIQCR which applies to any QCQP. Let be a QCQP. Our approach to solve is first to build an equivalent mixed-integer quadratic problem . This equivalent problem has a quadratic convex objective function, linear constraints, and additional variables y that are meant to satisfy the additional quadratic constraints , where x are the initial variables of problem . We then propose to solve by a branch-and-bound algorithm based on the relaxation of the additional quadratic constraints and of the integrality constraints. This type of branching is known as spatial branch-and-bound. Computational experiences are carried out on a total of 325 instances. The results show that the solution time of most of the considered instances is improved by our method in comparison with the recent implementation of QuadProgBB, and with the solvers Cplex, Couenne, Scip, BARON and GloMIQO. [ABSTRACT FROM AUTHOR]

This paper primarily focuses on an investigative study on allocating Flexible AC transmission system (FACTS) devices on radial distribution systems. The FACTS device that is used in this paper is static var compensator (SVC). In this paper, SVC is placed on each bus except substation bus and study the results to find the best location for allocation. A design of SVC was done by fixed reactance method. The total power loss of network and voltage of nodes have been analyzed for each allocation and the sequences of each implementation have been compared to find the best location of SVC. The proposed algorithm has been executed on IEEE 33-bus and 69-bus standard test systems. [ABSTRACT FROM AUTHOR]

Block allocation problem (BAP) is the key part of container terminal management. To make full use of the limited yard resources and to reduce the turnaround and transportation time, this paper focuses on the objectives of BAP offering multiple work ways, avoiding loading or collecting containers at the same time, and minimising the total distance between the berths and the blocks. The solution of BAP is similar to the model that individuals choose proper islands to live in the unified neutral theory of biodiversity and biogeography (UNTBB). The difficulty lies on that the UNTBB model is a complicated stochastic process. A novel algorithm based on the modified UNTBB model is proposed. Based on this modified UNTBB model, this paper proposed a grouping strategy to reduce the solution set. To guarantee the consecutive container groups not to put in the same block and to assure a consecutive and parallel loading process, a strategy that kills any one of the species existing prey relationship is put forward. Moreover, a greedy strategy is constructed to select an island to accelerate the ecological balance among islands. All these strategies are included into UNTBB to make ecological selection more instructive and faster. The optimal solution in the modified UNTBB model is the optimisation result of the BAP. The experiments show that the novel algorithm proposed in this paper achieves a better performance than the other conventional methods. [ABSTRACT FROM PUBLISHER]

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a dynamic resequencing problem covering realistic properties of a mixed-model assembly line. To this end, we present a mathematical model that addresses dynamically supplied blocking information and viable due dates. We developed two different strategies that use a static resequencing algorithm as a subroutine. One strategy integrates each unblocked order immediately into the planned sequence, whereas the other strategy waits for good positions that do not conflict with the due dates. All algorithms construct guaranteed feasible sequences. Using industrial test data, we show that both strategies perform significantly better than a simple method derived from practice. A replanning procedure that tries to improve the current planned sequence whenever computing time suffices yields an additional improvement for both strategies. [ABSTRACT FROM PUBLISHER]

Although the use of algorithms has become increasingly prominent in the digital market, such algorithms are often opaque and prone to risks of making biased decisions. Algorithms could also be used to harm competition, such as by facilitating cartels. Such developments make it necessary to examine the readiness of existing competition law to tackle cases involving algorithms. This paper focuses on analysing Indonesian competition law to address the following issues: (1) how current Indonesian competition law deals with algorithms-related cases; (2) which indicators could detect anti-competitive algorithms; and (3) which competition policy approach could be considered in Indonesia to tackle the problem resulted from the use of algorithms. [ABSTRACT FROM AUTHOR]

Following some recent research about fractional programming problems with absolute-value functions, in this paper, we consider this class of optimization problems. In the related literature, there exists a famous transformation which is used to convert the above-mentioned nonsmooth fractional optimization problem into a smooth one. This work presents some conditions under which the considered nonsmooth problem can fail to be equivalent to the obtained smooth one. [ABSTRACT FROM AUTHOR]

This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for predicting different diseases, using machine learning algorithms, has been done. The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT. Each technique was analyzed on each step, and the overall drawbacks are pointed out. In addition, it also analyzes the type of data used for predicting the concerned disease, whether it is the benchmark or manually collected data. Finally, research directions have been identified and depicted based on the various existing methodologies. This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws. [ABSTRACT FROM AUTHOR]

In this paper, we consider the incentive mechanism of a construction supply chain which includes a contractor and several subcontractors from both economic and environmental perspectives. Firstly, we describe the structure of the construction supply chain as well as the relationship between the contractor and subcontractors. Then, a bi-level nonlinear model with multiple followers comprising uncertain parameters is developed to balance the benefits of all supply chain members. In this model, the contractor is the leader while the subcontractors are followers. Next, we convert the primal model into a deterministic counterpart robust model, and a heuristic polynomial algorithm is designed to solve the transformed model. Finally, the validity of the model is verified by a numerical example. Our paper provides a method to quantitatively analyze construction projects from the perspective of supply chains while considering economic performance and environmental performance with the existence of uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal cascaded fractional order controller (CC-FOC) having disturbance observer (DOB) to minimize the frequency deviation of an autonomous wind-diesel-generator (WDG) system in the wake of load variation and random wind power output. The model of WDG consists of a variable-speed wind turbine (VSWT) and a diesel engine generator to match the ever-rising load demand. To regulate the frequency of the WDG system, fractional-order proportional-integral-derivative (FOPID) and tilt-integral-derivative (TID) controllers are simultaneously used as master and slave controllers, respectively, for developing the CC-FOC. An improved squirrel search algorithm (SSA) with quasi-oppositional-based learning (QOSSA) is applied to explore the optimum gains of the proposed non-integer controller. The performance of the developed controller is compared with the outputs of FOPID, TID, and PID controllers. The result demonstrates the mastery and effectiveness of the proposed controller against random load and wind power fluctuations. Finally, Kharitonov's stability theorem is applied to ensure the robustness margin of the studied WDG plant for a wide range of system parameters variation. [ABSTRACT FROM AUTHOR]

The primary aim of this paper is to develop an accurate stereo matching algorithm based on cost aggregation with adaptive support weight (CAASW). In this study, we use a pair of images (from left and right cameras) to find corresponding points. First, the truncated absolute difference is represented as cost computing, and the cost aggregation is completed with adaptive support weight. The winner take all method is then used to find the minimum cost aggregation value of the location in order to obtain the initial disparity. In order to enhance the accuracy of this study, a disparity map is employed, which uses continuity for disparity neighboring relationships; the histogram is represented as a disparity refinement, making it possible to reduce the disparity map's errors. In this paper, the CAASW can be divided into two parts. The first part is CABSW, a method employing binary target and reference images with an area of intersection to form an irregular adaptive support window. The second part is CAASW, using similarity and proximity as features of an adaptive support window with CABSW. In order to better represent the accuracy of this method, the experiment uses the Middlebury database, in addition to other methods, for comparison and analysis, to explore the experimental results and to obtain results with a lower percentage of unsatisfactory matching pixels. Future research will explore applications of this method in robot navigation, industrial manufacturing, human interface, three-dimensional reconstruction and improved computer intelligence capabilities. [ABSTRACT FROM AUTHOR]

Efficient software/hardware codesign is posing significant challenges to embedded systems. This paper proposes Codem, a software/hardware codesign flow for embedded systems, which models both processors and Intellectual Property (IP) cores as services. Tasks are regarded as abstract instructions which can be scheduled to IP cores for parallel execution automatically. In order to guide the hardware implementations of the hot spot functions, this paper incorporates a novel hot spot-based profiling technique to observe the hot spot functions while the application is being simulated. Furthermore, based on the hot spot of various applications, an adaptive mapping algorithm is presented to partition the application into multiple software/hardware tasks. We test the profiling-based design flow with classic Sort applications. Experimental results demonstrate that Codem can efficiently help researchers to identify the hot spots, and also outline a new direction to combine profiling techniques with state-of-the-art reconfigurable computing platforms for specific task acceleration. [ABSTRACT FROM PUBLISHER]

In this paper, a two-stage denoising algorithm is proposed. Complementary ensemble empirical mode decomposition based on permutation entropy (CEEMD-PE) is carried out for the noisy monitoring signal in the first stage. Several denoising models are established according to the intrinsic mode function obtained by CEEMD-PE. An objective function considers both the smoothness of the denoising model and the similarity between the denoising model and the noisy monitoring signal is established, and the second stage denoising is realized by solving the objective function. The denoising model corresponding to the optimal solution of the objective function is the smooth denoising model. In order to verify the correctness of the two-stage denoising algorithm, the mixed simulation signal with noise is denoised, and based on the definition of signal-to-noise ratio, the effect of two-stage denoising is calculated. Finally, the algorithm is applied to the actual blasting seismic signal denoising processing. It is found that the proposed algorithm can not only reduce the noise interference but also retain the real part of the original signal while filtering the noise. [ABSTRACT FROM AUTHOR]

A 3D model can be machined in sections by dividing with cutting slices vertical to the z-axis. This paper introduces a Boolean-based algorithm for planar profiles, which is a frequent implementation for overlapping elimination in CL paths generation. In the proposed algorithm, the profiles can be convex or concave, with or without islands. It is available for general planar profiles on contouring and pocketing machining. This algorithm searches for all intervals split by intersections of complicate planar profiles directly and transforms 2D transversal intersection problems into 1D interval identifications. It uses a simple but efficient odd-even determination based on interval-linked sets by tracking the intersections along the governing profile. Depending on the operation of Boolean union, intersection, and difference between two profiles, the solutions can be obtained under the same manipulation procedures. This proposed algorithm can be easily adapted to Boolean operations between regions composed of general closed profiles and be implemented on computerised CAD/CAM systems. Examples with various multiple profiles are demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we present a meta-heuristic algorithm for the resource-constrained project scheduling problem with discounted cash flows. We assume fixed payments associated with the execution of project activities and develop a heuristic optimisation procedure to maximise the net present value of a project subject to the precedence and renewable resource constraints. We investigate the use of a bi-directional generation scheme and a recursive forward/backward improvement method from literature and embed them in a meta-heuristic scatter search framework. We generate a large dataset of project instances under a controlled design and report detailed computational results. The solutions and project instances can be downloaded from a website in order to facilitate comparison with future research attempts. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic suspended established practices that, in normal times, are seen as central to the functioning of education systems. For example, in England, school closures led to the cancellation of national examinations in 2020, and their attempted replacement with an algorithmic model. Following public outcry about what were seen as the unjust effects of the application of that model, there was a very public policy reversal, and examination grades were awarded on the basis of moderated teacher assessments or Centre Assessed Grades, resulting in substantial grade inflation. This paper draws on research that investigated the actors involved in examinations policy in this period and focuses especially on the sources of expertise and the kinds of knowledge that were mobilised - or not - in the decision to cancel examinations, to develop the algorithm and to revert to Centre Assessed Grades. [ABSTRACT FROM AUTHOR]

This paper interrogates the fundamental logic of data-driven decision-making (DDDM) as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault's understanding of this concept, I center Colin Koopman's recent analysis of the 'informational person' to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining the background of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman's work in terms of how it may extend critical studies of educational datafication. [ABSTRACT FROM AUTHOR]

Many operational research (OR) techniques use historical data to populate model input parameters. Although the majority of these models take into account stochastic variation of the inputs, they do not necessarily take into account seasonal variations and other stochastic effects that might arise. One of the major applications of OR lies within healthcare, where ever increasing pressure on healthcare systems is having major implications on those who plan the provision of such services. Coping with growing demand for healthcare, as well as the volatile nature of the number of arrivals at a healthcare facility makes modelling healthcare provision one of the most challenging fields of OR. This paper proposes the use of a relatively modern time series technique, Singular Spectrum Analysis (SSA), to improve existing algorithms that give required staffing levels. The methodology is demonstrated using data from a large teaching hospital's emergency unit. Using time dependent queueing theory, as well as SSA, staffing levels are obtained. The performance of our technique is analysed using a weighted mean square error measure, introduced in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault detection problem for two-dimensional (2-D) discrete-time systems described by the Fornasini–Marchesini local state-space model. The goal of the paper is to design a fault detection filter to detect the occurrence of faults in finite-frequency domain. To this end, a finite-frequencyH−index is used to describe fault sensitivity performance, and a finite-frequencyH∞index is used to describe disturbance attenuation performance. In light of the generalised Kalman–Yakubovich–Popov lemma for 2-D systems and matrix inequality techniques, convex conditions are derived for this fault detection problem. Based on these conditions, a numerical algorithm is put forward to construct a desired fault detection filter. Finally, a numerical example and an industrial example are given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a detailed study of the application of Root-MUSIC algorithm in the continuous-time frequency modulation (CTFM) system. Classically, CTFM-based sonars and radars use the Discrete Fourier transform (DFT) magnitude peaks to find the range of the received echoes. In applications where the medium does not support large signal bandwidth over the ranges of interest, the DFT technique cannot provide the required range resolution. A technique called dual demodulator continuous-time frequency modulation (DDCTFM) was proposed to overcome this limitation and provides greater range resolution. But we observe that there is a problem of phase mismatch in DDCTFM that does not increase the range resolution. This problem is explained in the paper. The super resolution Root-MUSIC algorithm is studied in this context to evaluate its efficacy in providing higher frequency resolution and corresponding range resolution in CTFM. Several detailed simulation experiments done in MATLAB to provide insight into its performance are reported in the paper. Also, lab experiments have been done to practically demonstrate the efficacy of using Root-MUSIC technique in CTFM. Based on the results obtained from the simulation experiments, it is concluded that there is an improvement by a factor of 2.8 in the range resolution estimation by the use of Root-MUSIC algorithm over DFT-based analysis in CTFM. In practical experiments, the improvement factor obtained in the range resolution estimation is 2.35. [ABSTRACT FROM AUTHOR]

Three-phase grid-feeding converters are key components to integrate distributed generation and renewable power sources to the power utility. Conventionally, proportional integral and proportional resonant-based control strategies are applied to control the output power or current of a GFC. But, those control strategies have poor transient performance and are not robust against uncertainties and volatilities in the system. This paper proposes aH2/H∞-based control strategy, which can mitigate the above restrictions. The uncertainty and disturbance are included to formulate the GFC system state-space model, making it more accurate to reflect the practical system conditions. The paper uses a convex optimisation method to design theH2/H∞-based optimal controller. Instead of using a guess-and-check method, the paper uses particle swarm optimisation to search aH2/H∞optimal controller. Several case studies implemented by both simulation and experiment can verify the superiority of the proposed control strategy than the traditional PI control methods especially under dynamic and variable system conditions. [ABSTRACT FROM PUBLISHER]

The paper studies N-player linear quadratic differential games on an infinite time horizon with deterministic feedback information structure. It introduces two iterative methods (the Newton method as well as its accelerated modification) in order to compute the stabilising solution of a set of generalised algebraic Riccati equations. The latter is related to the Nash equilibrium point of the considered game model. Moreover, we derive the sufficient conditions for convergence of the proposed methods. Finally, we discuss two numerical examples so as to illustrate the performance of both of the algorithms. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to discuss the latest nano-biosensor technologies and existing signal analyser algorithm methods so that an automatic and portable nano-biosensor analyser can be realised. In this paper, the latest nano-biosensors are reviewed, and particular attention is given to sensors that provide amplitude changes at their output. To provide an automatic signal analysis of these changes, existing signal processing algorithms for peak detection are also discussed in detail. [ABSTRACT FROM AUTHOR]

There are several types of load in distribution system. The load value that represents the varying load with time is needed to be estimated for reconfiguration because the system is reconfigured in non-real time. Specially, as the power system develops as the smart grid, the amount of distributed generations is increased. Since the variability of distributed generations’ output is higher compared to normal generations, it is important that which representative value is used for variable output when an algorithm performs. In addition, the reversed current caused by distributed generation should be considered. In this paper, we propose a method to choose representative value considering distribution generation and variable load and also a constraint considering the reversed current. By this method, we study the distribution system reconfiguration for reliable and efficient operating. [ABSTRACT FROM AUTHOR]

Due to the serious speckle noise in synthetic aperture radar (SAR) image, segmentation of SAR images is still a challenging problem. In this paper, a novel region merging method based on perceptual hashing is proposed for SAR image segmentation. In the proposed method, perceptual hash algorithm (PHA) is utilized to calculate the degree of similarity between different regions during region merging in SAR image segmentation. After reducing the speckle noise by Lee filter which maintains the sharpness of SAR image, a set of different homogeneous regions is constructed based on multi-thresholding and treated as the input data of region merging. The new contribution of this paper is the combination of multi-thresholding for initial segmentation and perceptual hash method for the adaptive process of region merging, which preserves the texture feature of input images and reduces the time complexity of the proposed method. The experimental results on synthetic and real SAR images show that the proposed algorithm is faster and attains higher-quality segmentation results than the three recent state-of-the-art image segmentation methods. [ABSTRACT FROM AUTHOR]

A method of generating optimal tool paths for sculptured surface machining with flat-end cutters is presented in this paper. The inclination and tilt angles, as well as the feed directions of the cutter at each cutter contact point on a machining path are optimised as a whole so that the machining width of the tool path can be as large as possible, and concerns such as smooth cutter motion, gouging avoidance, scallop height and machining widths overlap are also considered when calculating a path. A multi-criteria tool path optimisation model is introduced, and it is converted into a single objective optimisation with the weighted sum method. The Differential Evolution (DE) algorithm is suitable for solving this highly non-linear problem. However, the searching process of the DE algorithm may be trapped in local minima due to large number of design variables. Therefore, an algorithm combining the DE algorithm and the sequence linear programming algorithm is developed to solve this optimisation model. The proposed method is applied to two freeform surfaces to illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a single-item capacitated lot-sizing problem in a flow-shop system with energy consideration is studied. The planning horizon is defined by a set of periods where each one is characterised by a length, an allowed maximal power, an electricity price, a power price and a demand. The objective is to determine the quantities to be produced by each machine at each period while minimising the production cost in terms of electrical, inventory, set-up and power required costs. For medium- and large-scale problems, lot-sizing problems are hard to solve. Therefore, in this study, two heuristics are developed to solve this problem in a reasonable time. To evaluate the performances of these heuristics, computational experiments are presented and numerical results are discussed and analysed. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of scheduling a set of n jobs on two identical machines with preparation constraints. Each job requires before its execution a set of resources and a non-negligible preparation time. The objective is to minimise the makespan. This problem is NP-hard. We prove the NP-hardness of two specific cases where in the first case preparation times take only three values, whereas in the second case preparation times and the release dates take only two values, respectively. Then, we present some special cases and heuristic algorithms along with an experimental study. [ABSTRACT FROM AUTHOR]

This paper makes contributions in the area of predictive control and in particular examines to what extent different methods for parameterising the degrees of freedom within the input trajectories can improve aspects of feasibility and performance. A few earlier papers have suggested the potential for Laguerre polynomials to be effective within a predictive control design, but without giving explicit design guidelines. This paper extends that work by looking not only at systematic choices for the Laguerre parameters but also other choices of orthogonal functions. Systematic mechanisms are discussed to choose the best tuned alternative parameterisation dynamics. The efficacy of the proposed parameterisations within existing predictive control algorithms are demonstrated by examples. [ABSTRACT FROM PUBLISHER]

This paper introduces a block-based motion estimation algorithm based on projection with Adaptive Window Size Selection (AWSS) along with its architecture. Motion field of pixel is either horizontal or vertical, this paper assume horizontal and all the processing is done based on it. This projection method is combined with AWSS in which appropriate search window for each block is determined on the basis of motion vectors and prediction errors obtained for the previous block, which makes this novel method several times faster than exhaustive search with negligible performance degradation. Encoding QCIF-size video by the proposed method results in reduction of computational complexity of motion estimation by roughly 45% and overall encoding by 23%, while maintaining image/video quality. A hardware architecture for the adaptive motion estimation algorithm was also done in this paper, which has several features such as low latency less area and more speed which is applicable for 30 frames per sec with frame size of QCIF (176 × 144) which has a small chip area of (3.57 × 3.57 mm2) and operating frequency of 83 MHz 1.8 v, 195 mW power dissipation designed for video coding, with UMC 0.18 μm 1P6M cell library. [ABSTRACT FROM AUTHOR]