3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Structural Analysis by Modified Signature Matrix for Integro-differential-algebraic Equations

Integro-differential-algebraic equations (IDAE)s are widely used in applications of engineering and analysis. When there are hidden constraints in an IDAE, structural analysis is necessary. But if derivatives of dependent variables appear in their integrals, the existing definition of the signature matrix for an IDAE cannot be satisfied. Moreover, if an IDAE has a singular Jacobian matrix after structural analysis by the Sigma-method, improved structural analysis methods are proposed to regularize it. However, the optimal value of an IDAE may be negative which can not ensure the termination of the regularization. Furthermore, overestimation of the signature matrix may also lead to failure of its structural analysis. In this paper, firstly, we redefine the signature matrix and introduce a definition of the degree of freedom for IDAEs. Thus, the termination of improved structural analysis methods can be guaranteed. Secondly, the detection method by points is proposed to deal with the problem of overestimation of signature matrix. Thirdly, the embedding method has proved to suitable for structural unamenable IDAEs, including those types that arise from symbolic cancellation and numerical degeneration. Finally, the global numerical method is applied to an example of two-stage drive system which can help to find all solutions for IDAEs by witness points. Hopefully, through the example of pendulum curtain, the approach for IDAEs proposed in this paper can be applied to integro-partial-differential-algebraic equations (IPDAE)s.Comment: 33 pages, 4 figures, conferenc

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.Comment: TOIS 202

Assessment of multi-air emissions: case of particulate matter (dust), SO2, NOx and CO2 from iron and steel industry of China

Industrial activities are generally energy and air emissions intensive, requiring bulky inputs of raw materials and fossil fuels and emitting huge waste gases including particulate matter (PM, or dust), sulphur dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2), and other substances, which are severely damaging the environment. Many studies have been carried out on the quantification of the concentrations of these air emissions. Although there are studies published on the co-effect of multi-air emissions, a more fair and comprehensive method for assessing the environmental impact of multi-air emissions is still lacking, which can simultaneously consider the flow rate of waste gases, the availability of emitting sources and the concentrations of all emission substances. In this work, a Total Environmental Impact Score (TEIS) approach is proposed to assess the environmental impact of the main industrial processes of an integrated iron and steel site located in the northeast of China. Besides the concentration of each air emission substance, this TEIS approach also combines the flow rate of waste gases and the availability of emitting sources. It is shown that the processes in descending order by the values of TEIS are sintering, ironmaking, steelmaking, thermal power, steel rolling, and coking, with the values of 17.57, 16.68, 10.86, 10.43, 9.60 and 9.27, respectively. In addition, a sensitivity analysis was conducted, indicating that the TEIS order is almost the same with the variation of 10% in the permissible CO2 concentration limit and the weight of each air emission substance. The effects of emitting source availability and waste gas flow rate on the TEIS cannot be neglected in the environmental impact assessment

SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

Nowadays, gathering high-quality training data from multiple data controllers with privacy preservation is a key challenge to train high-quality machine learning models. The potential solutions could dramatically break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academia researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short). These two solutions have their advantages and limitations when we evaluate them from privacy preservation, ways of communication, communication overhead, format of data, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of training machine learning models over multiple data sources with privacy-preserving (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, parties supported, data partitioning, threat model, and supported machine learning models, to show the advantages and limitations. Next, we introduce the state-of-the-art platforms which support online training over multiple data sources. Finally, we discuss the potential directions to resolve the problem of TMMPP.Comment: 17 pages, 4 figure

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Synthesis and Luminescence Properties of Nine Novel Carbazolyl Diacylhydrazone Schiff-bases

Nine novel carbazolyl diacylhydrazone Schiff-bases were synthesized by alkylation, F-C acylation and condensation reactions starting from carbazole and hydrazide. The title Schiff-bases were characterized by 1H NMR, MS, IR and elemental analysis. The synthetic conditions were optimized, and the best yield of the title Schiff-bases was up to 92.3 %. The relationships between the luminescence properties and the structures of the title Schiff-bases were studied. The results showed that the introduction of the Naphthalene-2-yloxy could form great plane conjugate structure to improve their luminescence properties