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With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master's in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. [ABSTRACT FROM AUTHOR]

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. [ABSTRACT FROM AUTHOR]

In popular discussions, the nuances of AI are often abridged as "the algorithm", as the specific arrangements of machine learning (ML), deep learning (DL) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness of algorithmic systems. Perhaps in response to concerns about "the algorithm" by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

This paper deals with the current Blockchain adopted in the various types of trading markets supporting smart contracts that process text-based transaction information only when exchanging cryptocurrencies. Even though the Blockchain itself is decentralized, services can still be provided through a centralized system in order to provide adequate services to the users. It also allows the Autochain to apply Blockchain technology to existing businesses conducted under the Fourth Industrial Revolution to realize higher productivity or competitiveness and improve profitability to a remarkable extent. The Autochain is adaptable to all Blockchain technologies, companies, or research works around the world to overcome the limitations of the existing Blockchain technology. It creates the Blockchain-platform ecology chain and ecosystem, making our lives more convenient while revolutionizing every industry. In addition, it automates all Blockchain-based systems and offers a base for constructing a system wherein everyone can easily provide, operate, and maintain Blockchain services. This paper discusses a number of Blockchain-related technologies and presents a decentralized Application (dApp) exclusively designed for mobile use, taking house rental service as an example. It also presents the existing Blockchain and the Blockchain database storage method and proposes the dApp based on it. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

During 2018, as part of a research project funded by the Deviant Practice Grant, artist Bruno Moreschi and digital media researcher Gabriel Pereira worked with the Van Abbemuseum collection (Eindhoven, NL), reading their artworks through commercial image-recognition (computer vision) artificial intelligences from leading tech companies. The main takeaways were: somewhat as expected, AI is constructed through a capitalist and product-focused reading of the world (values that are embedded in this sociotechnical system); and that this process of using AI is an innovative way for doing institutional critique, as AI offers an untrained eye that reveals the inner workings of the art system through its glitches. This paper aims to regard these glitches as potentially revealing of the art system, and even poetic at times. We also look at them as a way of revealing the inherent fallibility of the commercial use of AI and machine learning to catalogue the world: it cannot comprehend other ways of knowing about the world, outside the logic of the algorithm. But, at the same time, due to their "glitchy" capacity to level and reimagine, these faulty readings can also serve as a new way of reading art; a new way for thinking critically about the art image in a moment when visual culture has changed form to hybrids of human–machine cognition and "machine-to-machine seeing". [ABSTRACT FROM AUTHOR]

This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one of the main drivers of what has been described as the "Fourth Industrial Revolution", as well as the most innovative technology developed to date. It is a pervasive transformative innovation, which needs a new approach. In 2017, the European Parliament introduced the notion of the "electronic person", which sparked huge debates in philosophical, legal, technological, and other academic settings. The issues related to AI should be examined from an interdisciplinary perspective. In this paper, we examine this legal innovation—that has been proposed by the European Parliament—from not only legal but also technological points of view. In the first section, we define AI and analyse its main characteristics. We argue that, from a technical perspective, it appears premature and probably inappropriate to introduce AI personhood now. In the second section, justifications for the European Parliament's proposals are explored in contrast with the opposing arguments that have been presented. As the existing mechanisms of liability could be insufficient in scenarios where AI systems cause harm, especially when algorithms of AI learn and evolve on their own, there is a need to depart from traditional liability theories. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are booming in medicine, and the question of biases induced or perpetuated by these tools is a very important topic. There is a greater risk of these biases in radiology, which is now the primary diagnostic tool in modern treatment. Some authors have recently proposed an analysis framework for social inequalities and the biases at risk of being introduced into future algorithms. In our paper, we comment on the different strategies for resolving these biases. We warn that there is an even greater risk in mixing the notion of equity, the definition of which is socio-political, into the design stages of these algorithms. We believe that rather than being beneficial, this could in fact harm the main purpose of these artificial intelligence tools, which is the care of the patient. [ABSTRACT FROM AUTHOR]

There are growing interests in the use of robots in collaborative environments with humans or other intelligent machines. Sensing the environment for which the robot is operating can be done in many ways, generally guided by skin-like sensors. Some of the skins are inspired by natural sensing in humans or other species. As humans, we use many of our senses, such as version, hearing, smell, and touch, to move around by avoiding colliding with other humans or objects. Different from humans, many other mammals also use whiskers as an additional sensor to help navigate around. In this paper, we demonstrate a touchless capacitive imaging-based sensor in the situation where the obstacles are in close vicinity to the robot. The proposed imaging system can sense the changes in areas near to the skin-like sensors by measuring the capacitances between the array of electrodes. A 4D sensing approach has been developed with the spatiotemporal Total Variation algorithm. The 4D operational mode gives sensors the time awareness that allows for dynamical responses and hence the better control of the robots. Several experiments are conducted to show the skin-like behaviour of this sensor by simulating various scenarios. The sensor shows the excellent ability to detect an object in its vicinity, where the depth is close to half of the planar sensor array size. [ABSTRACT FROM AUTHOR]

Since the emergence of the innovative field of artificial intelligence (AI) in the 1960s, the late Hubert Dreyfus insisted on the ontological distinction between man and machine, human and artificial intelligence. In the different editions of his classic and influential book What computers can't do (1972), he posits that an algorithmic machine can never fully simulate the complex functioning of the human mind—not now, nor in the future. Dreyfus' categorical distinctions between man and machine are still relevant today, but their relation has become more complex in our increasingly data-driven society. We, humans, are continuously immersed within a technological universe, while at the same time ubiquitous computing, in the words of computer scientist Mark Weiser, "forces computers to live out here in the world with people" (De Souza e Silva in Interfaces of hybrid spaces. In: Kavoori AP, Arceneaux N (eds) The cell phone reader. Peter Lang Publishing, New York, 2006, p 20). Dreyfus' ideas are therefore challenged by thinkers such as Weiser, Kevin Kelly, Bruno Latour, Philip Agre, and Peter Paul Verbeek, who all argue that humans are much more intrinsically linked to machines than the original dichotomy suggests—they have evolved in concert. Through a discussion of the classical concepts of individuum and 'authenticity' within Western civilization, this paper argues that within the ever-expanding data-sphere of the twenty-first century, a new concept of man as 'aggregate of data' has emerged, which further erodes and undermines the categorical distinction between man and machine. This raises political and ethical questions beyond the confines of technology and artificial intelligence. Moreover, this seemingly never-ending debate on what computers should (or should not) do provokes the philosophical necessity to once again define the concept of what it is to be 'human.' [ABSTRACT FROM AUTHOR]

The aim of this paper is to present some philosophical considerations about the supposed AI emergence in the future. However, the predicted timeline of this process is uncertain. To avoid any kind of speculations on the proposed analysis from a scientific point of view, a metaphysical approach is undertaken as a modal context of the discussion. I argue that modal claim about possible AI emergence at a certain point of time in the future is justified from a temporal perspective. Therefore, worldwide society must be prepared for possible AI emergence and the expected profound impact of such an event on the existential status of humanity. [ABSTRACT FROM AUTHOR]

In Homo Deus, Yuval Noah Harari argues that technological advances of the twenty-first century will usher in a significant shift in how humans make important life decisions. Instead of turning to the Bible or the Quran, to the heart or to our therapists, parents, and mentors, people will turn to Big Data recommendation algorithms to make these choices for them. Much as we rely on Spotify to recommend music to us, we will soon rely on algorithms to decide our careers, spouses, and commitments. Harari also predicts that next, the state will take away individuals’ rights to make their own choices about their lives. If Google knows where your children would flourish best in school, why should the state allow a fallible human parent to decide? Liberalism—which, as Harari uses this term, refers to a state of society in which human freedom to choose is respected and championed—will collapse. In this paper, I argue that Harari’s conception of the future implications of recommendation algorithms is deeply flawed, for two reasons. First, users will not rely on algorithms to make decisions for them because they have no reason to trust algorithms, which are developed by companies with their own incentives, such as profit. Second, for most of our life decisions, algorithms will not be able to be developed, because the factors relevant to the decisions we face are unique to our situation. I present an alternative depiction of the future: instead of relying on algorithms to make decisions for us, humans will use algorithms to enhance our decision-making by helping us consider the most relevant choices first and notice information we might not otherwise. Finally, I will also argue that even if computers could make many of our decisions for us, liberalism as a political system would emerge unscathed. [ABSTRACT FROM AUTHOR]

This article is based on a roundtable held at the Association of Internet Researchers (AoIR) annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s (OII) Digital Ethics Lab (DELab). It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence (AI), in particular what ethical frameworks are needed to guide AI’s rapid development and increased use in societies. The paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: (1) its uses, (2) its users, (3) its designers, and (4) the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. [ABSTRACT FROM AUTHOR]

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. The results show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices. [ABSTRACT FROM AUTHOR]

Given one instance of an $\mathcal{NP}$ -hard optimization problem, can we tell in advance whether it is exactly solvable or not? If it is not, can we predict which approximate algorithm is the best to solve it? Since the behavior of most approximate, randomized, and heuristic search algorithms for $\mathcal{NP}$ -hard problems is usually very difficult to characterize analytically, researchers have turned to experimental methods in order to answer these questions. In this paper we present a machine learning-based approach to address the above questions. Models induced from algorithmic performance data can represent the knowledge of how algorithmic performance depends on some easy-to-compute problem instance characteristics. Using these models, we can estimate approximately whether an input instance is exactly solvable or not. Furthermore, when it is classified as exactly unsolvable, we can select the best approximate algorithm for it among a list of candidates. In this paper we use the MPE (most probable explanation) problem in probabilistic inference as a case study to validate the proposed methodology. Our experimental results show that the machine learning-based algorithm selection system can integrate both exact and inexact algorithms and provide the best overall performance comparing to any single candidate algorithm. [ABSTRACT FROM AUTHOR]

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a “parsing graph”, in a spirit similar to parsing sentences in speech and natural language. The algorithmconstructsthe parsing graph andre-configuresit dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches-generative(top-down) methods anddiscriminative(bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns-generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002.IEEE Trans. PAMI, 24(5):657-673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions. [ABSTRACT FROM AUTHOR]

Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow and tracking to layered motion, mosaic construction, and face coding. Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We examine which of the extensions to Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot. In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation. In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) methods aim at detecting the degradation, diagnosing the faults and predicting the time at which a system or a component will no longer perform its desired function. PHM is based on access to a model of a system or a component using one or combination of physical or data-driven models. In physical-based models, one has to gather a lot of knowledge about the desired system and then build an analytical model of the system function of the degradation mechanism that is used as a reference during system operation. On the other hand, data-driven models are based on the exploitation of symptoms or indicators of degradations using statistical or artificial intelligence methods on the monitored system once it is operational and learn the normal behaviour. Trend extraction is one of the methods used to extract important information contained in the sensory signals, which can be used for data-driven models. However, extraction of such information from the collected data in a practical working environment is always a great challenge as sensory signals are usually multidimensional and obscured by noise. Also, the extracted trends should represent the nominal behaviour of the system as well as the health status evolution. This paper presents a method for nonparametric trend modelling from multidimensional sensory data so as to use such trends in machinery health prognostics. The goal of this work is to develop a method that can extract features representing the nominal behaviour of the monitored component, and from these features, smooth trends are extracted to represent the critical component's health evolution over the time. The proposed method starts by multidimensional feature extraction from machinery sensory signals. Then, unsupervised feature selection on the features' domain is applied without making any assumptions concerning the number of the extracted features. The selected features can be used to represent the nominal behaviour of the system and hence detect any deviation. Then, empirical mode decomposition algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Finally, ridge regression is applied to the extracted trend for modelling and can be used later for the remaining useful life prediction. The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and another data set downloaded from NASA repository where it is shown to be able to extract signal trends. [ABSTRACT FROM AUTHOR]

Determination of critical voltage control areas (VCAs) is a very important task in both voltage stability assessment and control. However, it is impossible to detect VCAs in real time during appearance of emergency cases for large-scale power systems. Therefore, it is a reasonable solution to employ an artificial intelligent system (AIS) to detect VCAs and to identify the prone buses for monitoring and control purposes as quickly as possible. The training data must contain the simulation results and the historical data collected from a wide range of various emergency cases. Using this database, a clustering process which provides finite clusters of all possible VCAs and a classification function which affiliates each emergency or stress case to its own cluster of VCAs are the main stages to prepare AIS for automatic VCA identification. In this paper a novel data clustering method based on shuffled frog-leaping algorithm (SFLA) is presented for the first task. The results are finite numbers of clustered groups of VCAs with a representative vector of participation factors (PF) for each group. SFLA combines the benefits of the genetic-based memetic algorithms as well as social behavior-based particle swarm optimization methods. In present study the application of SFLA in data clustering is also compared with the most popular analytic algorithm of clustering, K-means, and also with genetic algorithm-based data clustering to demonstrate the validity of proposed clustering method. Numerical results are also presented for IEEE 14-bus test system and an artificial database. The comparative results show the effectiveness of proposed algorithm. [ABSTRACT FROM AUTHOR]

We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., local search algorithms, global search algorithms, higher-order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-bit parity and two-spiral problems. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg-Marquardt algorithm yields the lowest RMS error for the N-bit parity and the two-spiral problems, higher-order neuron algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the neuro-fuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos( x) and a few special functions like the gamma function, the complimentary error function and the upper tail cumulative x2-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg-Marquardt algorithm yields the best results. Keeping in view the highly nonlinear behaviour and the wide dynamic range of these functions, it is suggested that these functions can also be considered as standard benchmark problems for function approximation using artificial neural networks. [ABSTRACT FROM AUTHOR]

It is well known that one can collect the coefficients of five (or more) homographies between two views into a large, rank deficient matrix. In principle, this implies that one can refine the accuracy of the estimates of the homography coefficients by exploiting the rank constraint. However, the standard rank-projection approach is impractical for two different reasons: it requires many homographies to even score a modest gain; and, secondly, correlations between the errors in the coefficients will lead to poor estimates. In this paper we study these problems and provide solutions to each. Firstly, we show that the matrices of the homography coefficients can be recast into two parts, each consistent with ranks of only one. This immediately establishes the prospect of realistically (that is, with as few as only three or four homographies) exploiting the redundancies of the homographies over two views. We also tackle the remaining issue: correlated coefficients. We compare our approach with the “gold standard”; that is, non-linear bundle adjustment (initialized from the ground truth estimate—the ideal initialization). The results confirm our theory and show one can implement rank-constrained projection and come close to the gold standard in effectiveness. Indeed, our algorithm (by itself), or our algorithm further refined by a bundle adjustment stage; may be a practical algorithm: providing generally better results than the “standard” DLT (direct linear transformation) algorithm, and even better than the bundle adjustment result with the DLT result as the starting point. Our unoptimized version has roughly the same cost as bundle adjustment and yet can generally produce close to the “gold standard” estimate (as illustrated by comparison with bundle adjustment initialized from the ground truth). Independent of the merits or otherwise of our algorithm, we have illuminated why the naive approach of direct rank-projection is relatively doomed to failure. Moreover, in revealing that there are further rank constraints, not previously known; we have added to the understanding of these issues, and this may pave the way for further improvements. [ABSTRACT FROM AUTHOR]

Machine intelligence has been a research hotspot in mechatronics in recent years. This research presents a 2D/3D object recognition system for enhancing the intelligence of an industrial robot (KUKA robot). The image processing and object recognition algorithms were developed using software packages VisionPro and LabVIEW. Experiments were carried out to verify the performance of the system. It can be concluded that the system is able to recognise any general 2D object within a time of six seconds. The performance of the system in 3D object recognition is slower compared to 2D objects, which is largely affected by the number of trained images stored in the database, the complexity of the object, and the presence of similar objects in the database. Despite the complexity of the objects being recognised, both the overall accuracy and success rate of the system are close to 100%. The developed system proved to be robust and allows for automatic recognition of objects in the manufacturing environment described in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the automatic inspection of leadframes to detect stamping defects in the presence of translational and rotational misalignment. The algorithm uses several image processing operations such as blob analysis, morphological closing and image subtraction to detect the stamping defects. The algorithm has been successfully tested on leadframes having simulated and real defects on multiple modules. The proposed algorithm overcomes both translational and rotational misalignment of the leadframe, thus eliminating the need to synchronize the leadframe movement and camera image capture. The system can be integrated in the manufacturing line for inspecting both continuous reel and individual cut leadframes. [ABSTRACT FROM AUTHOR]

The n-job, m-machine job shop scheduling (JSS) problem is one of the general production scheduling problems. Many existing heuristics give solutions for small size problems with near optimal solutions. This paper deals with the criterion of makespan minimization for the job shop scheduling of different size problems. The proposed computational method of artificial immune system algorithm (AIS) is used for finding optimal makespan values of different size problems. The artificial immune system algorithm is tested with 130 benchmark problems [10 (ORB1-ORB5 & ARZ5-ARZ9), 40 (LA01-LA40) and 80 (TA01-TA80)]. The results show that the AIS algorithm is an efficient and effective algorithm which gives better results than the Tabu search shifting bottleneck procedure (TSSB) as well as the best solution of shifting bottleneck procedure ( SB-GLS1 ) of Balas and Vazacopoulos. [ABSTRACT FROM AUTHOR]

The single EWMA controller has been proven to have excellent performance for small disturbances in the run-to-run process. However, incorrect selection of the EWMA parameter can have the opposite effect on the controlled process output. An adaptive system is necessary to automatically adjust the controller parameters on-line in order to have better performance. In this study, a simple and efficient algorithm based on neural networks (NN) is proposed to minimise the inflation of the output variance on line. The authors have shown that the sequence of EWMA gains, generated by a NN-based adaptive approach, converges close to the optimal controller value under IMA (1, 1), step and trend disturbance models. The paper also shows that the NN-based adaptive EWMA controller has a superior performance than its predecessors. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On the one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object. Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance. The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice. [ABSTRACT FROM AUTHOR]