Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

The sudden and spontaneous occurrence of epileptic seizures can impose a significant burden on patients with epilepsy. If seizure onset can be prospectively predicted, it could greatly improve the life of patients with epilepsy and also open new therapeutic avenues for epilepsy treatment. However, discovering effective predictive patterns from massive brainwave signals is still a challenging problem. The prediction of epileptic seizures is still in its early stage. Most existing studies actually investigated the predictability of seizures offline instead of a truly prospective online prediction, and also the high inter-individual variability was not fully considered in prediction. In this study, we propose a novel adaptive pattern learning framework with a new online feature extraction approach to achieve personalized online prospective seizure prediction. In particular, a two-level online feature extraction approach is applied to monitor intracranial electroencephalogram (EEG) signals and construct a pattern library incrementally. Three prediction rules were developed and evaluated based on the continuously-updated patient-specific pattern library for each patient, including the adaptive probabilistic prediction (APP), adaptive linear-discriminant-analysis-based prediction (ALP), and adaptive Naive Bayes-based prediction (ANBP). The proposed online pattern learning and prediction system achieved impressive prediction results for 10 patients with epilepsy using long-term EEG recordings. The best testing prediction accuracy averaged over the 10 patients were 79, 78, and 82 percent for the APP, ALP, and ANBP prediction scheme, respectively. The experimental results confirmed that the proposed adaptive prediction framework offers a promising practical tool to solve the challenging seizure prediction problem.

Abstract With growing concern of data privacy violations, privacy preservation processes become more intense. The k-anonymity method, a widely applied technique, transforms the data such that the publishing datasets must have at least k tuples to have the same link-able attribute, quasi-identifiers, values. From the observations, we found that, in a certain domain, all quasi-identifiers of the datasets, can have the same data type. This type of attribute is considered as an Identical Generalization Hierarchy (IGH) data. An IGH data has a particular set of characteristics that could utilize for enhancing the efficiency of heuristic privacy preservation algorithms. In this paper, we propose a data privacy preservation heuristic algorithm on IGH data. The algorithm is developed from the observations on the anonymous property of the problem structure that can eliminate the privacy constraints consideration. The experiment results are presented that the proposed algorithm could effectively preserve data privacy and also reduce the number of visited nodes for ensuring the privacy protection, which is the most time-consuming process, compared to the most efficient existing algorithm by at most 21%.

Feature selection is at the heart of machine learning, and it is effective at facilitating data interpretability and improving prediction performance by defying the curse of dimensionality. Group feature selection is often used to reveal relationships in structured data and provide better predictive power compared with the standard feature selection methods without consideration of the grouped structure. We study a group feature selection problem in networked data in which edge weights are considered as features, while each node in the network is regarded as a group feature. This problem is particularly useful in feature selection for neuroimaging data, where the data are high dimensional and the intrinsic networked structure among the features (i.e., connectivities between regions) in brain data has to be captured properly. We propose a mathematical model based on the support vector machines (SVM), which entails the ℓ0norm regularization to restrict the number of nodes (i.e., groups). To cope with the computational challenge of the ℓ0norm regularization, we develop a convex relaxation reformulation of the proposed model as a convex semiinfinite programming (SIP). We then introduce a new iterative algorithm that achieves an optimal solution for this convex SIP. Experimental results for synthetic and real brain network data sets show that our approach gives better predictive performance compared with the state-of-the-art group feature selection and the standard feature selection methods. Our technique additionally yields a sparse subnetwork solution that is easier to interpret than those obtained by other methods.

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO's strong global search capability and LS's fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. It usually causes preventable hospitalizations and deaths, while associated with a huge amount of cost. Traditional preclinical in vitro safety profiling and clinical safety trials are restricted in terms of small scale, long duration, huge financial costs and limited statistical signifi- cance. The availability of large amounts of drug and ADR data potentially allows ADR predictions during the drugs’ early preclinical stage with data analytics methods to inform more targeted clinical safety tests. Despite their initial success, existing methods have trade-offs among interpretability, predictive power and efficiency. This urges us to explore methods that could have all these strengths and provide practical solutions for real world ADR predictions. We cast the ADR-drug relation structure into a three-layer hierarchical Bayesian model. We interpret each ADR as a symbolic word and apply latent Dirichlet allocation (LDA) to learn topics that may represent certain biochemical mechanism that relates ADRs with drug structures. Based on LDA, we designed an equivalent regularization term to incorporate the hierarchical ADR domain knowledge. Finally, we developed a mixed input model leveraging a fast collapsed Gibbs sampling method that the complexity of each iteration of Gibbs sampling proportional only to the number of positive ADRs. Experiments on real world data show our models achieved higher prediction accuracy and shorter running time than the state-of-the-art alternatives.

As a new advanced service-oriented networked manufacturing mode, cloud manufacturing (CMfg) is currently one of the main directions of development in the manufacturing industry. In this study, a resource modeling method oriented to CMfg is proposed to solve manufacturing resource data consistency problems, which are often caused by heterogeneity, diversity, and complexity of manufacturing resource. Motivated by the manufacturing resource modeling literature, we construct a classification model of manufacturing resource using a line indexing method and a manufacturing resource information model for CMfg from the perspective of resource information. The concept hierarchy structure and ontology logical reasoning technology are employed to create this semantic modeling. Based on Ontology Web Language, we propose a new manufacturing resource ontology model that encompasses a resource multilayer model and includes a physical, virtual, and cloud resource data layers, as well as a cloud end layer. We propose a searching and matching mechanism based on semantic similarity degree to locate the manufacturing resource and achieve accurate matching of resource service messages. Such mechanism is an efficient procedure for the manufacturing resource service in the CMfg platform. We present a case study of the proposed framework and model to illustrate validity and practicability of the proposed model.

Traditional time series analysis methods are limited on some complex real-world time series data. Respiratory motion prediction is one of such challenging problems. The memory-based nearest neighbor approaches haveshown potentials in predicting complex nonlinear time series compared to many traditional parametric prediction models. However, the massive time series subsequences representation, the similarity distance measures, the number of nearest neighbors, and the ensemble functions create challenges as well as limit the performance of nearest neighbor approaches in complex time series prediction. To address these problems, we propose a flexible time series pattern representation and selection framework, called the orthogonalpolynomial-based variant-nearest-neighbor (OPVNN) approach. For the respiratory motion prediction problem, the proposed approach achieved the highest and most robust prediction performance compared to the state-of-the-art time series prediction methods. With a solid mathematical and theoretical foundation in orthogonal polynomials, the proposed time series representation, subsequence pattern mining and prediction framework has a great potential to benefit those industry and medical applications that need to handle highly nonlinear and complex time series data streams, such as quasi-periodic ones.

Assessment of mental workload using physiological measures, especially electroencephalography (EEG) signals, is an active area. Recently, a number of wireless acquisition systems to measure EEG and other physiological signals have become available. Few studies have applied such wireless systems to assess cognitive workload and evaluate their performance. This paper presents an initial step to explore the feasibility of a popular wireless system (Emotiv EPOC headset) to assess memory workload levels in a well-known n-back task. We developed a signal processing and classification framework, which integrated an automatic artifact removal algorithm, a broad spectrum of feature extraction techniques, a personalized feature scaling method, an information-theory-based feature selection approach, and a proximal-support-vector-machine-based classification model. The experimental results show that the wirelessly collected EEG signals can be used to classify different memory workload levels for nine participants. The classification accuracies between the lowest workload level (0-back) and active workload levels (1-, 2-, 3-back) were close to 100%. The best classification accuracy for 1- versus 2-back was 80%, and 1- versus 3-back was 84%. This study indicates that the wireless acquisition system and the advanced data analytics and pattern recognition techniques are promising to achieve real-time monitoring and identification of mental workload levels for humans engaged in a wide variety of cognitive activities in the modern society.

Recreating the natural anatomy in ligament reconstruction is crucial to fully restore the knee joint function and reduce impingement on iatrogenic injury to adjacent structures, yet is subject to the difficulties in locating ligament and other associated soft tissues insertion sites intraoperatively and the high interperson morphological variability cross patients. In this study, we present a new quantitative analysis method capable of achieving personalized identification of cruciate ligament and soft tissue insertions. We craft patient-specific features of tibia outline that can be accurately and reliably measured from CT images. In addition, we propose a supervised structure learning and prediction model with special interdimensional and response structure regularization terms to capture relationship between the spatial arrangement of soft tissue insertions and the patient-specific features extracted from the tibia outlines. In the experiment, the proposed model outperforms baseline models and provides an accurate and accessible approach that can be used as the first and the most critical step to achieve personalized surgical planning in cruciate ligament reconstruction.

The traditional k-nearest neighbor (KNN) method classifies an object by the majority vote of its neighbors, and only one parameter k is to be optimized. We propose a decomposed version of the KKN (DKNN) method, which classifies an unseen object based on the distances to the centroids of the KNNs. DKNN has a training process to learn the local-optimafree distance metric by solving a convex optimization problem. The optimization problem not only learns a metric that minimizes classification errors and maximizes the margin between intraclass and interclass distances. In addition, it also selects important features and removes irrelevant features when L1 regularization is incorporated in the optimization model. The feature selection component is extremely useful for high-dimensional classification problems. Tested on public datasets, the proposed DKNN is competitive and often outperforms traditional machine learning algorithms.

Establishing family relationships, such as parentage and sibling relationships, is fundamental in biological research, especially in wild species, as they are often important to understanding evolutionary, ecological, and behavioral processes. Because it is commonly impossible to determine familial relationships from field observations alone, the reconstruction of sibling relationships often depends on informative genetic markers coupled with accurate sibling reconstruction algorithms. Most studies in the literature reconstruct sibling relationships using methods that are based on either statistical analyses (i.e., likelihood estimation) or combinatorial concepts (i.e., Mendelian inheritance laws) of genetic data. We present a novel computational framework that integrates both combinatorial concepts and statistical analyses into one sibling reconstruction optimization model. To solve this integrated model, we propose a column-generation approach with a branch-and-price method. Under the assumption of parsimonious reconstruction, the master problem is to find the minimum set of sibling groups to cover the tested population. Pricing subproblems, which include both statistical similarity and combinatorial concepts of genetic data, are iteratively solved to generate high-quality sibling group candidates. Tested on real biological data sets, our approach efficiently provides reconstruction results that are more accurate than those provided by other state-of-the-art reconstruction algorithms.

The link prediction problem is an emerging real-life social network problem in which data mining techniques have played a critical role. It arises in many practical applications such as recommender systems, information retrieval, and marketing analysis of social networks. We propose a new mathematical programming approach for predicting a future network using estimated node degree distribution identified from historical data. The link prediction problem is formulated as an integer programming problem that maximizes the sum of link scores (probabilities) with respect to the estimated node degree distribution. The performance of the proposed framework is tested on real-life social networks, and the computational results show that the proposed approach can improve the performance of previously published link prediction methods.

Numerical typing errors can lead to serious consequences, but various causes of human errors and the lack of contextual clues in numerical typing make their prediction difficult. Human behavior modeling can predict the general tendency in making errors, while data mining can recognize neurophysiological feedback in detecting cognitive abnormality on a trial-by-trial basis. This study suggests integrating human behavior modeling and data mining to predict human errors because it utilizes both 1) top-down inference to transform interactions between task characteristics and conditions into a general inclination of an average operator to make errors and 2) bottom-up analysis in parsing psychophysiological measurements into an individual's likelihood of making errors on a trial-by-trial basis. Real-time electroencephalograph (EEG) features collected in a numerical typing experiment and modeling features produced by an enhanced human behavior model (queuing network model human processor) were combined to improve error classification performance by a linear discriminant analysis (LDA) classifier. Integrating EEG and modeling features improved the results of LDA classification by 28.3% in keenness (d') and by 10.7% in the area under ROC curve (AUC) from that of using EEG only; it also outperformed the other three benchmarking scenarios: using behaviors only, using apparent task features, and using task features plus trial information. The AUC was significantly increased from using EEG along only if EEG + Model features were used.

This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

We study a difficult real life scheduling problem encountered in oil and petrochemical industry, involving inventory and distribution operations, which requires integrated scheduling. The problem itself is NP-complete, however we show some special cases, and propose polynomial time solution methods. These could be used as a starting point for a heuristic making use of these simplified cases. This study proposes two alternative approaches for the main problem, one of them making use of one of the special cases using minimum cost flow formulation, and the other one using Bender’s Decomposition once the problem is reformulated to make it easier to handle. Both results show promising results and computation time. Bender’s Decomposition approach allows exact solutions to be found in a much faster fashion.

Discrete k-median (DKM) clustering problems arise in many real-life applications that involve time-series data sets, in which nondiscrete clustering methods may not represent the problem domain adequately. In this study, we propose mathematical programming formulations and solution methods to efficiently solve the DKM clustering problem. We develop approximation algorithms from a bilinear formulation of the discrete k-median problem using an uncoupled bilinear program algorithm. This approximation algorithm, which we refer to as DKM-L, is composed of two alternating linear programs, where one can be solved in linear time and the other is a minimum cost assignment problem. We then modify this algorithm by replacing the assignment problem with an efficient sequential algorithm for a faster approximation, which we call DKM-S. We also propose a compact exact integer formulation, DKM-I, and a more efficient network design-based exact mixed-integer formulation, DKM-M. All of our methods use arbitrary pairwise distance matrices as input. We apply our methods to simulated single-variate and multivariate random walk time-series data. We report comparative clustering performances using normalized mutual information (NMI) and solution speeds among the DKM methods we propose. We also compare our methods to other clustering algorithms that can operate with distance matrices, such as hierarchical cluster trees (HCT) and partition around medoids (PAM). We present NMI scores and classification accuracies of our DKM algorithms compared to HCT and PAM using five different distance measures on simluated data, as well as public benchmark and real-life neural time-series data sets. We show that DKM-S is much faster than HCT, PAM, and all other DKM methods and produces consistently good clustering results on all data sets.

Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number of voxels (features) far exceeds the number of stimulus instances/data observations. To avoid model overfitting, there is a need to select informative voxels before building a classification model. In this paper, we propose a robust feature (voxel) selection framework using mutual information (MI) and partial least square regression (PLS) to establish an informativeness index for prioritizing selection of voxels based on the degree of their association to the experimental conditions. We evaluated the robustness of our proposed framework by assessing performance of standard classification algorithms, when combined with our feature selection approach, in a publicly-available fMRI dataset of object-level representation widely used to benchmark MVPA performance (Haxby, 2001). The computational results suggest that our feature selection framework based on MI and PLS drastically improves the classification accuracy relative to those previously reported in the literature. Our results also suggest that highly informative voxels may provide meaningful insight into the functional-anatomic relationship of brain activity and stimulus conditions.

Material safety and traceability is of great importance in toy manufacturing because there have been tougher requirements on toy product safety imposed by new international regulations. We investigate and analyze the production workflow in small and medium toy manufacturing enterprises by SADT and simulation analysis. We find out that tracking information is incomplete and information flow and material flow are out-sync due to lacking material and production process collaboration in current system. Thus, the tracking objective creates a need for systems to collaborate material flow and production flow in manufacturing enterprises. In this paper, aiming at enhancing efficiency of production, a new concept of collaborative material and production tracking based on a supply chain view is presented. Then we also propose collaborative architecture of toy material and production tracking system based on the Internet of Things to improve tracking accuracy and efficiency. Finally, we take the paint material for example to implement the production tracking system. The application example shows that it can collect real-time data accurately and reliably. This system has practical significance in improving product quality management and realizing enterprise information construction.

Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures. Being able to predict impending seizures could greatly improve the lives of patients with epilepsy. In this study, we propose a new adaptive learning approach for online seizure prediction based on analysis of electroencephalogram (EEG) recordings. For each individual patient, we construct baseline patterns of normal and preseizure EEG samples, continuously monitor sliding windows of EEG recordings, and classify each window to normal or preseizure using a $(K)$-nearest-neighbor (KNN) method. A new reinforcement learning algorithm is proposed to continuously update both normal and preseizure baseline patterns based on the feedback from prediction result of each window. The proposed approach was evaluated on EEG data from 10 patients with epilepsy. For each one of the 10 patients, the adaptive approach was trained using the recordings containing the first half of seizure occurrences, and tested prospectively on the subsequent recordings. Using a 150-minute prediction horizon, our approach achieved 73 percent sensitivity and 67 percent specificity on average over 10 patients. This result is shown to be far better than those of a nonupdate prediction scheme and two native prediction schemes.

Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end.

There is an urgent need for a quick screening process that could help neurologists diagnose and determine whether a patient is epileptic versus simply demonstrating symptoms linked to epilepsy but actually stemming from a different illness. An inaccurate diagnosis could have fatal consequences, particularly in operating rooms and intensive care units. Electroencephalogram (EEG) has been traditionally used, as a gold standard, to diagnose patients by evaluating those brain functions that might correspond to epilepsy and other brain disorders. This research therefore focuses on developing new classification techniques for multichannel EEG recordings. Two time-series classification techniques, namely, Support Feature Machine (SFM) and Network-Based Support Vector Machine (SVM) (NSVM), are proposed in this paper to predict from EEG readings whether a person is epileptic or nonepileptic. The SFM approach is an optimization model that maximizes classification accuracy by selecting a group of electrodes (features) that has strong class separability based on time-series similarity measures and correctly classifies EEG samples in the training phase. The NSVM approach integrates a new network-based model for multidimensional time-series data with traditional SVMs to exploit both the spatial and temporal characteristics of EEG data. The proposed techniques are tested on two EEG data sets acquired from ten and five patients, respectively. Compared with other commonly used classification techniques such as SVM and decision trees, the proposed SFM and NSVM techniques provide very promising and practical results and require much less time and memory resources than traditional techniques. This study is a necessary application of data mining to advance the diagnosis and treatment of human epilepsy.

This paper studies the applications of data mining techniques in early detection of numerical typing errors by human operators through a quantitative analysis of multichannel electroencephalogram (EEG) recordings. Three feature extraction techniques were developed to capture temporal, morphological, and time-frequency (wavelet) characteristics of EEG data. Two most commonly used data mining techniques, namely, linear discriminant analysis (LDA) and support vector machine (SVM), were employed to classify EEG samples associated with correct and erroneous keystrokes. The leave-one-error-pattern-out and leave-one-subject-out cross-validation methods were designed to evaluate the in- and cross-subject classification performances, respectively. For the in-subject classification, the best testing performance had a sensitivity of 62.20% and a specificity of 51.68%, which were achieved by SVM using morphological features. For the cross-subject classification, the best testing performance was achieved by LDA using temporal features, based on which it had a sensitivity of 68.72% and a specificity of 49.45%. In addition, the receiver operating characteristic (ROC) analysis revealed that the averaged values of the area under ROC curves of LDA and SVM for the in- and cross-subject classifications were both greater than 0.60 using the EEG 300 ms prior to the keystrokes. The classification results of this study indicated that the EEG patterns of erroneous keystrokes might be different from those of the correct ones. As a result, it may be possible to predict erroneous keystrokes prior to error occurrence. The classification problem addressed in this study is extremely challenging due to the very limited number of erroneous keystrokes made by each subject and the complex spatiotemporal characteristics of the EEG data. However, the outcome of this study is quite encouraging, and it is promising to develop a prospective early detection system for erroneous keystrokes based on brain-wave signals.

Epileptic seizure prediction is still a very challenging and unsolved problem for medical professionals. The current bottleneck of seizure prediction techniques is the lack of flexibility for different patients with an incredible variety of epileptic seizures. This study proposes a novel self-adaptation mechanism which successfully combines reinforcement learning, online monitoring and adaptive control theory for seizure prediction. The proposed method eliminates a sophisticated threshold-tuning/optimization process, and has a great potential of flexibility and adaptability to a wide range of patients with various types of seizures. The proposed prediction system was tested on five patients with epilepsy. With the best parameter settings, it achieved an averaged accuracy of 71.34%, which is considerably better than a chance model. The autonomous adaptation property of the system offers a promising path towards development of practical online seizure prediction techniques for physicians and patients.

With improved tools for collecting genetic data from natural and experimental populations, new opportunities arise to study fundamental biological processes, including behavior, mating systems, adaptive trait evolution, and dispersal patterns. Full use of the newly available genetic data often depends upon reconstructing genealogical relationships of individual organisms, such as sibling reconstruction. This paper presents a new optimization framework for sibling reconstruction from single generation microsatellite genetic data. Our framework is based on assumptions of parsimony and combinatorial concepts of Mendel's inheritance rules. Here, we develop a novel optimization model for sibling reconstruction as a large-scale mixed-integer program (MIP), shown to be a generalization of the set covering problem. We propose a new heuristic approach to efficiently solve this large-scale optimization problem. We test our approach on real biological data as presented in other studies as well as simulated data, and compare our results with other state-of-the-art sibling reconstruction methods. The empirical results show that our approaches are very efficient and outperform other methods while providing the most accurate solutions for two benchmark data sets. The results suggest that our framework can be used as an analytical and computational tool for biologists to better study ecological and evolutionary processes involving knowledge of familial relationships in a wide variety of biological systems.

This study focuses on the information extraction from reported sensor data in the communication system of wide-area-search munitions (WASMs). Such sensor data could be erroneous and inconsistent. For example, two WASMs might detect the same target, but associate it with two different targets and tracks. Similarly, two WASMs might detect two distinct targets, but recognize them as the same target. The research challenge is how to fuse both accurate and inaccurate information broadcasted from WASMs, and reconstruct the battle space for accurate target tracking. For each of the detected target points, WASMs provide its location information, detection time, and directional velocity. We, herein, propose a target clustering approach to group target points detected by WASMs and identify the track of individual targets. Our approach differs from traditional clustering techniques as it performs clustering using the time and orientation information, in addition to the distance in the Euclidean space. Our approach employs a network modeling technique to reconstruct all target points and their feasible movement, and a new optimization technique to find the most probable target tracks. Our approach can also determine the optimal number of clusters (targets) automatically from the input data. In this study, distributed interactive simulation, a real-time simulation of a network's information exchange, is used to generate battle space test instances that are used in evaluating the proposed framework. Based on seven realistically simulated instances, the computational results show that our approach provides extremely accurate target-tracking results in a timely fashion. We also compare our results with those obtained using the k-means clustering technique. On average, our approach reconstructs the real target tracks with about 95% accuracy in less than 10 s, while the k-means clustering results yields about 80% accuracy in a similar computational time.

As electronic records (e.g., medical records and technical defect records) accumulate, the retrieval of a record from a past instance with the same or similar circumstances, has become extremely valuable. This is because a past record may contain the correct diagnosis or correct solution to the current circumstance. We refer to the two records of the same or similar circumstances asmasterandduplicaterecords. Current record retrieval techniques are lacking when applied to this special master defect record retrieval problem. In this study, we propose a new paradigm for master defect record retrieval using network-based feature association (NBFA). We train the master record retrieval process by constructing feature associations to limit the search space. The retrieval paradigm was employed and tested on a real-world large-scale defect record database from a telecommunications company. The empirical results suggest that the NBFA was able to significantly improve the performance of master record retrieval, and should be implemented in practice. This paper presents an overview of technical aspects of the master defect record retrieval problem, describes general methodologies for retrieval of master defect records, proposes a new feature association paradigm, provides performance assessments on real data from a telecommunications company, and highlights difficulties and challenges in this line of research that should be addressed in the future.

The integrated production and distribution problem with bidirectional flows is a complicated optimization problem, usually with large problem sizes when encountered in practice. In this study, we propose a partial linear programming relaxation-based heuristic approach to solve a variation of this problem. The approach is called a partial relaxation in the sense that it relaxes the integer requirements only on selected variables. We also report on the gaps between the optimal solution and the heuristic solution provided by this partial relaxation, including analytical gaps for a special case and empirical gaps for randomly generated test cases. Our study of this problem was motivated by the operational planning problem of a medical equipment leasing network that involves a forward flow for new and refurbished devices and a reverse flow for used devices to be returned to suppliers over a multiple time-period planning horizon.

In this study, a novel multidimensional time series classification technique, namely support feature machine (SFM), is proposed. SFM is inspired by the optimization model of support vector machine and the nearest neighbor rule to incorporate both spatial and temporal of the multi-dimensional time series data. This paper also describes an application of SFM for detecting abnormal brain activity. Epilepsy is a case in point in this study. In epilepsy studies, electroencephalograms (EEGs), acquired in multidimensional time series format, have been traditionally used as a gold-standard tool for capturing the electrical changes in the brain. From multi-dimensional EEG time series data, SFM was used to identify seizure pre-cursors and detect seizure susceptibility (pre-seizure) periods. The empirical results showed that SFM achieved over 80% correct classification of per-seizure EEG on average in 10 patients using 5-fold cross validation. The proposed optimization model of SFM is very compact and scalable, and can be implemented as an online algorithm. The outcome of this study suggests that it is possible to construct a computerized algorithm used to detect seizure pre-cursors and warn of impending seizures through EEG classification.

Epilepsy is one of the most common brain disorders, but the dynamical transitions to neurological dysfunctions of epilepsy are not well understood in current neuroscience research. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this study is to develop and present a novel classification technique that is used to classify normal and abnormal (epileptic) brain activities through quantitative analyses of electroencephalogram (EEG) recordings. Such technique is based on the integration of sophisticated approaches from data mining and signal processing research (i.e., chaos theory,k-nearest neighbor, and statistical time series analysis). The proposed technique can correctly classify normal and abnormal EEGs with a sensitivity of 81.29% and a specificity of 72.86%, on average, across ten patients. Experimental results suggest that the proposed technique can be used to develop abnormal brain activity classification for detecting seizure precursors. Success of this study demonstrates that the proposed technique can excavate hidden patterns/relationships in EEGs and give greater understanding of brain functions from a system perspective, which will advance current diagnosis and treatment of epilepsy.

The distribution of broadcast TV across large provider networks has become a highly topical subject as satellite distribution capacity exhausts and competitive pressures increase. In a typical IPTV architecture, broadcast TV is distributed from two sources (for redundancy) to multiple destinations. The aim of this paper is to examine how IPTV can be reliably and cost effectively supported in wavelength division multiplexed (WDM) networks. WDM networks have evolved to mesh topologies and recently to support multicast, which is particularly valuable in reducing the network cost in broadcast TV applications. Our goal is to find two trees with a minimal total cost such that we have two physically (or Shared Risk Link Group (SRLG)) diverse paths to each of the destinations - one from each of the sources. Any two links that belong to a common SRLG are subject to a single point of failure, be it a channel or wavelength failure, a fiber cut, or a complete conduit cut. We first show that our path protection routing problem is NP-complete. We then propose an Integer Programming (IP) formulation for this problem. Using real network topology data, we show that the real networks are amenable to the IP problem formulation and yield optimal solutions.