Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Multi-sensor data fusion is an evolving technology whereby data from multiple sensor inputs are processed and combined. The data derived from multiple sensors can, however, be uncertain, imperfect, and conflicting. The present study is undertaken to help contribute to the continuous search for viable approaches to overcome the problems associated with data conflict and imperfection. Sensor readings, represented by belief functions, have to be fused according to their corresponding weights. Previous studies have often estimated the weights of sensor readings based on a single criterion. Mono-criteria approaches for the assessment of sensor reading weights are, however, often unreliable and inadequate for the reflection of reality. Accordingly, this work opts for the use of a multi-criteria decision aid. A modified Analytical Hierarchy Process (AHP) that incorporates several criteria is proposed to determine the weights of a sensor reading set. The approach relies on the automation of pairwise comparisons to eliminate subjectivity and reduce inconsistency. It assesses the weight of each sensor reading, and fuses the weighed readings obtained using a modified average combination rule. The efficiency of this approach is evaluated in a target recognition context. Several tests, sensitivity analysis, and comparisons with other approaches available in the literature are described.     

Conflict minerals (3TG): Mining production,applications and recycling

The growing public awareness of conflict minerals used in consumer electronics and various other devices (e.g. GPS in vehicles, medical equipment, airplane equipment and instruments, etc.) is accompanied by increasing regulatory obligations for companies affected by different laws and certification standards e.g. Dodd Frank Act section 1502, the ICGLR Regional Certification, the OECD Due Diligence Guidance or the planned EU Certification regulation.The so called conflicts minerals are natural mineral resources such as tin, tantalum, tungsten and Gold (3TG) that are critical resources for the electronic and automotive industries but also for other industries (aviation, medical equipment, etc.).Extraction of the 3TGs is widespread and includes primary industrial mining and artisanal mining as well as recycling.     

A 4D-sequencing approach for air traffic management

The current air traffic system is forecasted to face strong challenges due to the continuous increase in air traffic demand. Hence, there is a need for new types of organization permitting a more efficient air traffic management, with both a high capacity and a high level of safety, and possibly with a reduced environmental impact. In this article, we study a holistic approach, consisting in designing across Europe a very organized air traffic system, as opposed to free flight, to reduce costs while maintaining safety.     

Fuzzy Multi-fractional Programming for Land Use Planning in Agricultural Production System

In this paper, we present a multi-objective linear fractional programming (MOLFP) approach for multi-objective linear fuzzy goal programming (MOLFGP) problem. Here, we consider a problem in which a set of pair of goals are optimized in ratio rather than optimizing them individually. In particular, we consider the optimization of profit to cash expenditure and crop production in various seasons to land utilization as a fractional objectives and used remaining goals in its original form. Further, the goals set in agricultural production planning are conflicting in nature; thus we use the concept of conflict and nonconflict between goals for computation of appropriate aspiration level. The method is illustrated on a problem of agricultural production system for comparison with Biswas and Pal [1] method to show its suitability.     

Belief functions combination without the assumption of independence of the information sources

This paper considers the problem of combining belief functions obtained from not necessarily independent sources of information. It introduces two combination rules for the situation in which no assumption is made about the dependence of the information sources. These two rules are based on cautious combinations of plausibility and commonality functions, respectively. The paper studies the properties of these rules and their connection with Dempster’s rules of conditioning and combination and the minimum rule of possibility theory.     

Supply chain scheduling: Just-in-time environment

We study the benefits of coordinated decision making in a supply chain consisting of a manufacturer, a distributor, and several retailers. The distributor bundles finished goods produced by the manufacturer and delivers them to the retailers to meet their demands. The distributor is responsible for managing finished goods inventory. An optimal production schedule of the manufacturer, if imposed on the distributor, may result in an increased inventory holding cost for the distributor. On the other hand, an optimal distribution schedule of the distributor, if imposed on the manufacturer, may result in an increased production cost for the manufacturer. In this paper we develop mathematical models for individual optimization goals of the two partners and compare the results of these models with the results obtained for a joint optimization model at the system level. We investigate the computational complexities of these scheduling problems. The experimental results indicate that substantial cost savings can be achieved at the system level by joint optimization. We also study conflict and cooperation issues in the supply chain. The cost of conflict of a supply chain partner is a measure of the amount by which the unconstrained optimal cost increases when a decision is to be made under the scheduling constraint imposed by the other partner. We quantify these conflicts and show that the cost of conflicts are significant. We also show that a cooperative decision will generate a positive surplus in the system which can be shared by the two partners to make cooperation and coordination strategy more attractive.     

Novel evolutionary models and applications to sequence alignment problems

In this paper, we present a novel graph-theoretical approach for representing a wide variety of sequence analysis problems within a single model. The model allows incorporation of the operations “insertion”, “deletion”, and “substitution”, and various parameters such as relative distances and weights. Conceptually, we refer the problem as the minimum weight common mutated sequence (MWCMS) problem. The MWCMS model has many applications including multiple sequence alignment problem, the phylogenetic analysis, the DNA sequencing problem, and sequence comparison problem, which encompass a core set of very difficult problems in computational biology. Thus the model presented in this paper lays out a mathematical modeling framework that allows one to investigate theoretical and computational issues, and to forge new advances for these distinct, but related problems. Through the introduction of supernodes, and the multi-layer supergraph, we proved that MWCMS is -complete. Furthermore, it was shown that a conflict graph derived from the multi-layer supergraph has the property that a solution to the associated node-packing problem of the conflict graph corresponds to a solution of the MWCMS problem. In this case, we proved that when the number of input sequences is a constant, MWCMS is polynomial-time solvable. We also demonstrated that some well-known combinatorial problems can be viewed as special cases of the MWCMS problem. In particular, we presented theoretical results implied by the MWCMS theory for the minimum weight supersequence problem, the minimum weight superstring problem, and the longest common subsequence problem. Two integer programming formulations were presented and a simple yet elegant decomposition heuristic was introduced. The integer programming instances have proven to be computationally intensive. Consequently, research involving simultaneous column and row generation and parallel computing will be explored. The heuristic algorithm, introduced herein for multiple sequence alignment, overcomes the order-dependent drawbacks of many of the existing algorithms, and is capable of returning good sequence alignments within reasonable computational time. It is able to return the optimal alignment for multiple sequences of length less than 1500 base pairs within 30 minutes. Its algorithmic decomposition nature lends itself naturally for parallel distributed computing, and we continue to explore its flexibility and scalability in a massive parallel environment.     

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition

A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided.     

The impact of disaster on the strategic interaction between company and government

A company allocates a resource between safety effort and production. The government earns taxes on production. The disaster probability is modeled as a contest between the disaster magnitude and the two players’ safety efforts. The model illustrates that safety efforts are strategic substitutes and inverse U shaped in the disaster magnitude. The company’s safety effort increases, and the government’s safety effort decreases, in taxation. Taxation can ameliorate companies’ free riding on governments’ safety efforts. With sufficiently large production, the government prefers, and the company does not prefer, raising taxation above 0%. For the government, an upper limit usually exists above which taxation cannot be profitably increased. The model shows how both or no players exert safety efforts when the disaster magnitude is small and large respectively, and how they free ride on each other’s safety efforts when the disaster magnitude is intermediate. The company free rides when the unit production cost is low so that the large profits outweigh the negative impact of the disaster. With endogenized taxation determined by the government, the tax rate decreases in the disaster magnitude, the unit production cost, the government’s unit cost of safety effort, and how the company is negatively affected by the disaster. The tax rate increases in the company’s resource and how the government is negatively affected by the disaster. The tax rate is weakly U shaped in the company’s unit safety effort. The model is illustrated with numerical examples and with the oil spill disasters by BP in 2010 and by Exxon Valdez in 1989.     

When conflict induces the expression of incomplete preferences

Multicriteria conflict arises in pairwise comparisons, where each alternative outperforms the other one on some criterion, which imposes a trade-off. Comparing two alternatives can be difficult if their respective advantages are of high magnitude (the attribute spread is large). In this paper, we investigate to which extent conflict in a comparison situation can lead decision makers to express incomplete preferences, that is, to refuse to compare the two alternatives, or to be unable to compare them with confidence. We report on an experiment in which subjects expressed preferences on pairs of alternatives involving varying conflicts. Results show that depending on whether the participants are allowed to express incomplete preferences or not, attribute spread has a different effect: a large attribute spread increases the frequency of incomparability statements, when available, while it increases the use of indifference statements when only indifference and preference answers are permitted. These results lead us to derive some implications for preference elicitation methods involving comparison tasks.