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Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

The publication of Capital in the Twenty-First Century by Piketty (2014) propelled the debate about the prospects of the evolution of income and wealth inequalities in this century. One of the main controversies is about the effects on the income and wealth inequalities of a decrease in the growth rate g. In Piketty (2014), it is claimed that a decrease in g will cause an increase in the wealth inequality, through an increase in the difference r−g, where r is the rate of return on capital. This claim was criticized by many authors. In this chapter, the author presents a neoclassical growth model with heterogeneous agents and uses it to shed more light on this issue. The author’s model generalizes and improves previous models introduced in Piketty and Zucman (2015) and in Aoki and Nirei (2016). The author also presents a result, relating income, wealth, and wage inequalities.

This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.

Let G=(V,E) be a graph. The complement of G is the graph G¯:=(V,[V]2 \ E) where [V]2 is the set of pairs {x,y} of distinct elements of V. If K is a subset of V, the restriction of G to K is the graph G↾K:=(K,[K]2∩E). We prove that if G=(V,E) is a graph and k is an integer, 2 ≤ k ≤ v−2, then there is a k -element subset K of V such that e(G¯↾K)≠e(G↾K), moreover the condition k ≤ v−2 is optimal. We also study the case e(G¯↾K) ≢ e(G↾K)(mod p) where p is a prime number. Following a question from M.Pouzet, we show this: Let G=(V,E) be a graph with v vertices. If e(G) ≠ e(G¯) (resp. e(G)=e(G¯)) then there is an increasing family (Hn)2≤n≤v (resp. (Hn)2≤n≤v−2) of n -element subsets Hn of V such that e(G↾Hn)≠ e(G¯↾Hn) for all n. Similarly if e(G) ≢ e(G¯) (mod p) where p is a prime number, p>v−2, then there is an increasing family (Hn)2≤n≤v of n -element subsets Hn of V such that e(G↾Hn) ≢ e(G¯↾Hn)(mod p) for all integer n∈{2,3,…,v}.

This study examines the relative efficiencies of anti-poverty policies implemented in 28 Chinese provinces.

This study uses meta-frontier undesirable dynamic two-stage data envelopment analysis. The authors divide the poverty reduction process into two stages: agricultural production and poverty reduction. Public expenditure is the input for the second stage, and the population below the poverty line is the undesirable output. The authors compute the efficiencies (overall efficiency, efficiency of each stage and the efficiencies of individual inputs and outputs) using meta-frontier analysis for the 28 provinces.

The results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has been exacerbated.

The results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has exacerbated.

A large amount of attention and public resources are devoted to fighting poverty and associated market failures in China. The extant literature focuses either on the agricultural production itself or the relationship between human capital and productivity levels. Making use of recent developments of the DEA method, the authors propose a new framework for evaluating the efficiencies of the poverty reduction process. Such a framework has the advantage of giving researchers and policymakers a more detailed diagnosis with regard to the components in the endeavor to eliminate poverty and providing useful information for policymakers to optimize public funds use. Methodologically, the framework is flexible enough to be employed for future research in similar appraisals, at different geographic and scale aggregation levels, for public projects including but not limited to poverty reduction.

The objective of this study is to present a successful strategy to promote Korea as the logistics hub of Northeast Asia. Firstly, we introduce the necessity of a logistic hub strategy for Korea. In order to develop a successful strategy, we consider the 'free trade zone' model of the Netherlands and China. We develop a realistic and suitable model for Korea based on this Jree trade zone' model. Our proposal for a successful logistic hub strategy for Korea is the fulfillment of the following six objectives: 1) security of market accessibility, 2) improvement of the logistic system, 3) improvement of the education system, 4) improvement of the tax system, 5) assurance of labor market flexibility, and 6) development of an appealing living environment for foreigners.

In this paper, an adaptive fuzzy sliding mode controller (AFSMC) is developed for the formation control of a team of autonomous underwater vehicles (AUVs) subjected to unknown payload mass variations during their mission.

A sliding mode controller (SMC) is designed to drive the state trajectories of the AUVs to a switching surface in the state space. The payload mass variation results in parameter variation in AUV dynamics leading to actuator failure. This further leads to loss of communication among the members of the team. Hence, an adaptive SMC based on fuzzy logic is developed to maintain the coordinated motion of AUVs with payload mass variation.

The results are obtained by employing adaptive SMC for AUVs with and without payload variations and are compared. It is observed that the proposed adaptive SMC exhibits improved performance and tracks the desired trajectory in less time even with variation in the payload. The adaptive fuzzy control algorithm is developed to handle variation in payload mass variation. Lyapunov theory is used to establish stability of AFSMC controller.

Perfect alignment is assumed between centres of gravity (OG) and buoyancy (OB), thus AUVs maintaining horizontal stability during motion. The AUVs’ body centres are aligned with centres of gravity (OG), thus the distance vector being rg = [0,0,0]T. As it is a tracking problem, sway motion cannot be neglected as the AUVs are travelling in a curved locus, hence susceptible to Coriolis and centripetal forces. The AUV is underactuated as only two thrusters at the stern plate that are employed for the surge and yaw controls and error in Y- direction are controlled by adjusting control input in surge and heave direction. Control inputs to the thruster are constants, and depth control is achieved by adjusting the rudder angle.

AUVs are employed in military mission or surveys, and they carry heavy weapons or instrument to be deployed at or picked from specific locations. Such tasks lead to variation in payload, causing overall mass variation during an AUV’s motion. A sudden change in the mass after an AUV release or pick load results in variation in depth and average velocity.

The proposed controller can be useful for military missions for carrying warfare and hydrographic surveys for deploying instruments.

A proposed non-linear SMC has been designed, and its performances have been verified in terms of tracking error in X, Y and Z directions. An adaptive fuzzy SMC has been modelled using quantized state information to compensate payload variation. The stability of AFSMC controller is established by using Lyapunov theorem, and reachability of the sliding surface is ensured.

This paper aims to investigate the problem of on-line orbit planning and guidance for an advanced upper stage.

The double impulse optimal transfer orbit is planned by the Lambert algorithm and the improved particle swarm optimization (IPSO) method, which can reduce the total velocity increment of the transfer orbit. More specially, a simplified formula is developed to obtain the working time of the main engine for two phases of flight based on the theorem of impulse. Subsequently, the true anomalies of the start position and the end position for both two phases are planned by the Newton iterative algorithm and the Kepler equation. Finally, the first phase of flight is guided by a novel iterative guidance (NIG) law based on the true anomaly update with respect to the geometrical relationship. Also, a completely analytical powered explicit guidance (APEG) law is presented to realize orbital injection for the second phase of flight.

Simulations including Monte Carlo and three typical orbit transfer missions are carried out to demonstrate the efficiency of the proposed scheme.

A novel on-line orbit planning algorithm is developed based on the Lambert problem, IPSO optimization method and Newton iterative algorithm. The NIG and APEG are presented to realize the designed transfer orbit for the first and second phases of flight. Both two guidance laws achieve higher orbit injection accuracies than traditional guidance laws.

Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.