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Designers of shipboard power systems must consider many tradeoffs when selecting generators in the early-stage set-based design. This is not just an “equipment problem” based on physical design requirements such as limits of size and weight. The operating strategy for generators must be considered when evaluating the goodness of the design. To this end, this paper develops a design approach to impose system-level controls onto generating plants to evaluate their optimality. Two critical design metrics-fuel consumption and quality of service (QOS) are defined to incorporate these control variables, and co-optimized to reflect mission-oriented system performance. The minimum fuel consumption is computed based on a new concept of economic dispatch algorithm, which takes active and reactive power balance, and system redundancy requirements into account. QOS metric is defined to reflect system-level control strategies based on the classical concept of mean-time-between-failure, which is defined to depend inversely on the power produced. We develop a multi-objective particle swarm optimization to effectively locate the Pareto fronts of system performance for all design alternatives over a whole given mission. Thus a set of optimal design alternatives can be rapidly selected to represent the non-dominated optimal compromises of the two metrics. This design approach is demonstrated and compared with a single-objective design approach in a design example of shipboard generating plant design. And more optimal alternatives are able to be found.

Unmanned cars' developing involves at least three major sections: environment perception, decision-making and subsequent vehicle control. Whereas the first and the latter have proven almost-solved problems, decision-making is considered to be the most important and difficult section in unmanned cars. In fact, no matter normal or unmanned cars, to ensure their safety, appropriate decision must be made timely by either human driver or computer. In this article, we present a decision-making method based on drivable area cutting. We considered the most common elements in actual driving process including traffic signs, traffic lights, pedestrians, vehicles and even other vehicles' lights and horns to cut drivable area. We name it safe area for this cut-drivable area. Then decision is made based on the safe area. Experiment results show that our method works well in the highway and can be used in different environment with little change.

This paper presents Spirit, a lightweight programming framework for intelligent system. Ease of use and tiny development effort are two key features considered during the design. Spirit is mainly a template class, which provides a configure table, commonly used libraries in system control and computer vision, interprocess communication for parallel programming, hardware middleware for different hardware products, and customizable algorithm functions. Developers utilize Spirit to create modules or even systems for their intelligent applications very quickly as well as using powerful tools in Spirit for convenient debugging. We compared Spirit with existing robot frameworks and proposed a new design criterion. Results show that Spirit gives a better experience on rapid system design and debugging. We used it when developing an intelligent vehicle system.

Safety is the foremost quality to unmanned cars. In order to ensure the safety, unmanned cars must percept the surrounding environment precisely and exhaustively. To achieve this, various sensors including camera, lidar, and radar are equipped with unmanned cars. While the environment perception algorithms are relatively mature, there is no general solution for the multi-sensor information fusion for unmanned cars. In this article, we present a solution for multi-sensor information fusion for unmanned cars using radar map. With this solution, different environment information detected by various sensors can fuse naturally in radar map. Besides, the radar map is essentially a matrix and can be easily stored in memory. Experiment results show that radar map works well in all road conditions. And software development practices for unmanned cars also show that radar map can provide well support to decision-making, path planning and other subsequent sections.

In this paper, we present our recent efforts on developing a physical environment for performing traffic experiments. The two main characteristics of this environment are: (1) the whole environment is scaled down from the real traffic, (2) the traffic behaviors are performed by numbers of miniature autonomous vehicles. Performing traffic experiments in an actual environment has long been a hard problem. Moreover, modeling traffic phenomena is also a tough task, due to the complexity of the natural traffic system. However, with the rapid development of autonomous vehicle technology, we have an opportunity to improve these problems from a new perspective. That is using autonomous vehicles to perform traffic experiments. But with the limitations in cost and land availability, directly using full size autonomous vehicles also seemed unrealistic. Thus, we built a 1/10 scaled-down traffic system (SDTS) with more than 50 miniature autonomous vehicles. The environmental design, autonomous vehicles developing, agent modeling, traffic control, and real-time monitoring is considered systematically during system design. The SDTS can be used as a repeatable, appraisable, and verifiable experimental platform for traffic researches, such as testing traffic solutions, verifying key technologies in intelligent vehicles, and performing experiments about ITS. By now, the SDTS has supported a series of workshops, exchange activities and competitions in China.

A simulation-based approach to optimal design of systems requires collective solution to three related problems: selection of the best equipment, selection of the best system configuration, and optimal operation of the system in a mission-oriented sense. We solve these three problems by applying an improved Particle Swarm Optimization (PSO) algorithm on top of a toolbox that rapidly instantiates system models from high level specifications and system templates. The improved PSO more efficiently handles the involvement of discrete binary variables in the objective functions with universal constraints, more effectively avoids premature convergence, and yields a more accurate search for the global optimum solution. Our simulation-component-based co-optimization approach to system designs is illustrated by applying it to the design of an electric ship power system while accounting for the unit commitment problem during a set of missions. First, we prove the efficacy of the new PSO algorithm on one candidate system, and then we apply the new PSO method to evaluate and compare three candidate designs. The improved PSO shows that the system should actually consume up to 52% less fuel than is predicted by a simpler evaluation that invokes a proportionally-distributed power alignment; this is a much better measure of the true system performance. As compared with another validated hybrid PSO-GA algorithm, the improved PSO consistently finds an optimum operating point that yields up to 2.4% better fuel efficiency, and it improves the reliability of solutions by up to 32%. Next, three candidate designs are evaluated under optimal operating conditions, for a given mission profile, and compared in terms of their best performance. The simulation results successfully determine the best genset combination from the competing designs.

Lane-change is considered to be one of the toughest challenges in the field of autonomous vehicles. Any vehicles that are driving around the host vehicle may effect this operation. To ascertain the safety during the lane-change process, a comprehensive understanding of surrounding environments as well as a real-time decision is needed. Many researchers have proposed several approaches based on kinematics to perform this task. However, human drivers does not concerned too much about the kinematic parameters of vehicles, but try to control vehicles mainly based on the scenes from vision and experiences accumulated from long-time driving. From this perspective, we propose a more human-like lane-change system, whose sensors mainly based on five monocular cameras. Information from these sensors are fused into MOR(My Own Range) which means the drivable area for the vehicles. Finally, two fuzzy controllers are used to imitate expert behaviors and responses upon vehicle control. To verify the efficiency of the proposed lane-change system, a test platform, which includes ten scaled-down autonomous vehicles and a scaled model highway, is built. With this platform, tons of ideas or tests, especially of some dangerous tests, can be examined and implemented with a low cost later.

A multiplayer gaming environment, combining the multi-disciplinary engineering simulation software, Virtual Test Bed (VTB), and the gaming tool, Unity3D, is introduced to realize the analysis of energy storage for electric/hybrid vehicle through a vehicle driving game. The gaming engine couples the three-dimensional (3D) multibody vehicle system, especially developed in VTB at proper level of complexity for electrical engineers, with the real road information, exported from Google Earth to the 3D visual front-end. Several types of powertrain with energy storage system (ESS) are realized in VTB while advanced users can also conveniently construct their own design from the multi-disciplinary component library. Since the vehicle system is controlled instantaneously from user's gaming controller instead of preloaded sequential events, this gaming environment can be used to study the design of ESS in more real and complicated scenarios or help an individual vehicle owner plan his recharging events before his actual travel.

Autonomous vehicles are very hot research area which advance artificial intelligent, pattern recognition, computer vision, sensor fusion and control theory. In the paper we demonstrate a kind of micro-autonomous vehicle, describing hardware and software architectures, perception system and control method-cloud control. Our goal is study interaction behaviors between autonomous vehicles and how to take appropriate measure to avoid driving conditions hazardous. We propose a new control alogrithm-cloud control algorithm and experiments show cloud control algorithm has good performance and flexibility.

A fabrication method of ultra-sensitive cantilevers with high yield of almost 100% is presented. The oxide layer of an SOI wafer is patterned before the bulk silicon etching. It would break at the patterned place, while the bulk etching was to the end, which could avoid the cantilevers breaking. The SOI wafer is putted in a PTFE container during etching and cleaning. We moved the container from liquid to liquid instead of moving the wafer. These two steps ensured the high yield of almost 100% of the cantilevers. The cantilevers could detect forces as small as 10−17 N/Hz1/2 with dimensions of 465×10×0.47μm3 in a vacuum chamber at room temperature. The force sensitivity is believed to be much better at low temperature. The field gradient of three types of magnets is researched based on simulation. A conical magnet with small dimensions would be a good choice for spin moment detection in MRFM applications.

Service mining is an important technology which could help service composition. This paper mainly studied the problem of mining frequent composite services from the log of the composition-oriented service discovery system. First, a composite service representation method based on n-tuple was proposed. Second, a frequent composite service mining algorithm was put forward. Finally, an experiment was given to show the effectiveness of the mining algorithm.

One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the content of Web service, reasoning of service request and service matching, through classification and subsumption of concept, this paper retrieve and extract Web service content. Meanwhile, use weighted bipartite graph to match user request and published Web service, so as to enhance Web service discovery ability.