Dynamic movement primitives (DMPs) provide an effective method of learning manipulation skills from human demonstration. DMPs can be especially useful for imitating industrial manipulation tasks which are performed by humans and are difficult to model, for instance, deformable object manipulation. In this work the effectiveness of a conventional Cartesian space DMP is enhanced using a compact and efficient representation of dual quaternions (DQ). We demonstrate that our DQ based DMP learning approach that utilizes the geometrical meaning of screw-based kinematics, outperforms traditional decoupled task-space DMPs in terms of accuracy during learning in certain situations. Our DMP formulation affords two additional applications: (1) Filter the noisy and irregular sensing of human demonstration; (2) Limit the robotic manipulator's task-space velocity during teleoperation, thus improving the safety of the robot and the environment. The learning and filtering strategies are validated on a bimanual robotic system and a motion capture system. We demonstrate the effectiveness of DMP based manipulation of deformable object by learning a bimanual deformation trajectory and then using it to perform the same task in new scenarios.

In industrial sector, certain demands that are associated with operations of pumps are accompanied by reservoirs with storage capabilities for chemicals or solvents. Consequently, such industries possess inherent demand flexibility that can be utilized. This work proposes the coordinated control of industrial pump loads for participation in Singapore’s Demand Response (DR) programme by adjusting the electrical demand for revenue and power grid benefits. A novel Transactive Energy based energy management system (EMS) is proposed for coordinated operation scheduling of such industrial pumps to become responsive to electricity market prices. The optimal scheduling problem here is formulated based on detailed mathematical model of a water distribution system and a constrained linear programming based control technique is proposed. Case studies are performed in MATLAB environment using Singapore’s electricity market data and proposed EMS controlling the water distribution system. Results show that the proposed EMS is effective in shifting electrical demand away from peak price periods according to real Singapore market price signals while maintaining operational constraints, which leads to significant reduction in electricity costs. Further, in case of DR activations, significant demand may be reduced during DR target periods and revenue from the DR market participation is found to be lucrative.

Modern buildings have multiple distributed electrical resources (DERs) such as renewable energy sources and energy storage devices, flexible loads, and utility connection. Coupled with smart meters and new energy pricing plans, these physical assets present new opportunities for building operators to save energy, reduce operational costs, avoid carbon emissions and improve occupant comfort. The concept of Energy Nodes is proposed to represent the electrical resources and loads within a building and their electrical interactions, which can be programmed/controlled through software. A scaled-down hardware emulation test-bed representing commercial buildings is developed with commercial DER interfaces, controllable loads and Transactive Energy based Energy Management System to realize a cyber-physical Energy Node. Through studies on this test-bed, it is demonstrated that the proposed energy nodes can automatically act based on building operator's economy preferences, save electricity bills, and provide Demand Response service by reacting to electricity price signals from the markets.

This paper presents a framework for designing scalable and sustainable electrification solutions for off-grid electrification in energy deficient areas. A case study is presented on the development of a community microgrid solution in an energy deficient village on the Sumba island in Indonesia, based on the available renewable energy sources, batteries and a small diesel generator. The target of this off-grid solution is empowering the existing communities and development of new businesses. The microgrid solution also supports the basic community services (such as drinking water, schools, primary health centres) and household needs. This framework can be deployed in any community by customizing the type of energy sources and income generating loads according to the available resources. Validation of the proposed microgrid through simulation and test-bed emulation is reported. A solar microgrid implemented in this island is utilized to study the economic and social impact of electrification.

Air-conditioning and mechanical ventilation (ACMV), power distribution systems, elevators, and lighting systems are critical electrical assets of a smart building. Based on electrical components of these assets such as electrical motors, transformers, solid-state lighting and power electronic converters, a detailed analysis of failure mechanisms and statistics is presented in this paper. Further, suitable electrical and mechanical signals along with prevalent condition monitoring methods are suggested for condition monitoring of these assets based on the presented statistical analysis. This paper lays down the necessary foundation to understand the operating model of critical electrical equipment in buildings and provides a holistic picture for implementation of a predictive and preventive maintenance paradigm. This will enable the building operators to identify potential failures of building equipment and their evolution in real-time, driving decisions on scheduling repair or replacement of components to minimize downtime, save maintenance costs, improve occupant satisfaction, save energy, and maximize resilience.

In this paper, we describe an exploration system that was implemented by the search-advertising team of a prominent web-portal to address the cold ads problem. The cold ads problem refers to the situation where, when new ads are injected into the system by advertisers, the system is unable to assign an accurate quality to the ad (in our case, the click probability). As a consequence, the advertiser may suffer from low impression volumes for these cold ads, and the overall system may perform sub-optimally if the click probabilities for new ads are not learnt rapidly. We designed a new exploration system that was adapted to search advertising and the serving constraints of the system. In this paper, we define the problem, discuss the design details of the exploration system, new evaluation criteria, and present the performance metrics that were observed by us.

Synchronization is an area that exhibits rich hardware-software interactions in multiprocessors.It was studied extensively using microbenchmarks a decade ago.However, its performance implications are not well understood on modern systems or on real applications.We study the impact of synchronization primitives and algorithms on a modern, 64processor, hardware-coherent shared address space multiprocessor: the SGI Origin 2000.In addition to the actual results on a modern system, we examine the key methodological issues in studying synchronization, for both microbenchmarks and applications.We find that although the efficient hardware support (Fetch&Op) for synchronization provided on our machine usually helps lock and barrier microbenchmarks, it does not help in improving application performance when compared to good software algorithms that use the processor-provided LL-SC instructions.This is true even in applications that spend a significant amount of time in synchronization operations.More elaborate hardware support is unlikely to have a significant benefit either.From the applications' perspective, it is usually the waiting time due to load imbalance or serialization that dominates synchronization time, not the overhead of the synchronization operations themselves, even in apparently balanced cases where the overhead may be expected to be substantial.

Cache-coherent multiprocessors with distributed shared memory are becoming increasingly popular for parallel computing. However, obtaining high performance on these machines mquires that an application execute with good data locality. In addition to making efiective use of caches, it is often necessary to distribute data structures across the local memories of the processing nodes, thereby reducing the latency of cache misses.We have designed a set of abstractions for performing data distribution in the context of explicitly parallel programs and implemented them within the SGI MIPSpro compiler system. Our system incorporates many unique features to enhance both programmability and performance. We address the former by providing a very simple programmming model with extensive support for error detection. Regarding performance, we carefully design the user abstractions with the underlying compiler optimizations in mind, we incorporate several optimization techniques to generate efficient code for accessing distributed data, and we provide a tight integration of these techniques with other optimizations within the compiler Our initial experience suggests that the directives are easy to use and can yield substantial performance gains, in some cases by as much as a factor of 3 over the same codes without distribution.

Several cache-coherent shared-memory multiprocessors have been developed that are scalable and offer a very tight coupling between the processing resources. They are therefore quite attractive for use as compute servers for multiprogramming and parallel application workloads. Process scheduling and memory management, however, remain challenging due to the distributed main memory found on such machines. This paper examines the effects of OS scheduling and page migration policies on the performance of such compute servers. Our experiments are done on the Stanford DASH, a distributed-memory cache-coherent multiprocessor. We show that for our multiprogramming workloads consisting of sequential jobs, the traditional Unix scheduling policy does very poorly. In contrast, a policy incorporating cluster and cache affinity along with a simple page-migration algorithm offers up to two-fold performance improvement. For our workloads consisting of multiple parallel applications, we compare space-sharing policies that divide the processors among the applications to time-slicing policies such as standard Unix or gang scheduling. We show that space-sharing policies can achieve better processor utilization due to the operating point effect, but time-slicing policies benefit strongly from user-level data distribution. Our initial experience with automatic page migration suggests that policies based only on TLB miss information can be quite effective, and useful for addressing the data distribution problems of space-sharing schedulers.

Hardware distributed shared memory (DSM) systems efficiently support fine grain sharing of data by maintaining coherence at the level of individual cache lines and providing automatic replication in processor caches. Software DSM systems, on the other hand, amortize high communication costs by maintaining coherence at coarser granularities and replicating data at the level of local main memories. Even though software DSM systems have traditionally been targeted towards loosely coupled environments, some of the techniques are potentially useful in the context of tightly coupled multiprocessors. In particular, communicating data at a coarse grain can sometimes be more efficient than transferring the data as individual cache lines. Furthermore, replication in local memories can accommodate applications with larger working sets as compared to replication in processor caches only. Therefore, combining the two techniques in a hybrid protocol can potentially exploit the benefits of each approach.

Large-scale shared memory multiprocessors typically support a multilevel memory hierarchy consisting of per-processor caches, a local portion of shared memory, and remote shared memory. On such machines, the performance of parallel programs is often limited by the high latency of remote memory references. In this paper we explore how knowledge of the underlying memory hierarchy can be used to schedule computation and distribute data structures, and thereby improve data locality. Our study is done in the context of COOL, a concurrent object-oriented language developed at Stanford. We develop abstractions for the programmer to supply optional information about the data reference patterns of the program. This information is used by the runtime system to distribute tasks and objects so that the tasks execute close (in the memory hierarchy) to the objects they reference.