Target Path Research Articles

PENGARUH MOTIVASI KERJA, DISIPLIN DAN ETOS KERJA TERHADAP PERILAKU KEPEMIMPINAN PADA KANTOR PELAYANAN PEMBENDAHARAAN NEGARA (KPPN) KOTA PALEMBANG

Abstract
 
 Motivation is an encouragement to someone in order to achieve the desired goal. With the motivation to improve employee performance and work discipline in the company. The purpose of this study was to determine whether work motivation has a direct effect on employee performance and to determine whether work discipline mediates the effect of work motivation on employee performance at the Palembang City State Treasury Service Office (KPPN). The research variables used were work motivation, discipline and work ethic as independent variables and employee performance as the dependent variable. Data analysis used multiple linear regression. The results of the study are in line with the theoretical study used in this study, namely for the work motivation variable in accordance with Herzberg's theory that motivation can form a person who can improve performance in achieving organizational goals, for the leadership style variable in line with the Target Path theory developed by Robert House where Leadership can help employees to improve their performance. As for the work discipline variable, it is in line with Hasibuan's opinion where work discipline will affect the performance of the employee concerned. Thus, the results of the study show that the variables of work motivation, work discipline and work ethic have a significant influence on leadership at KPPN Palembang City.
 
 Keywords: Motivation, Work Discipline, Work Ethic, and leadership behavior

Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation

In this work, we investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. We use a multi-objective genetic algorithm to evolve response thresholds for a simulated swarm engaged in dynamic task allocation problems: two-dimensional and three-dimensional collective tracking. We show that evolved thresholds not only outperform uniformly distributed thresholds and dynamic thresholds but achieve nearly optimal performance on a variety of tracking problem instances (target paths). More importantly, we demonstrate that thresholds evolved for some problem instances generalize to all other problem instances, eliminating the need to evolve new thresholds for each problem instance to be solved. We analyze the properties that allow these paths to serve asuniversal training instancesand show that they are quite natural.Aftera priorievolution, the response thresholds in our system are static. The problem instances solved by the swarms are highly dynamic, with schedules of task demands that change over time with significant differences in rate and magnitude of change. That the swarm is able to achieve nearly optimal results refutes the common assumption that a swarm must be dynamic to perform well in a dynamic environment.

Economic Growth Targets and Carbon Emissions: Evidence from China.

Carbon emissions have become a new threat to sustainable development in China, and local government actions can play an important role in energy conservation and emission reduction. This paper explores the theoretical mechanisms and transmission paths of economic growth targets affecting carbon emissions from the perspective of economic growth targets and conducts an empirical analysis based on 30 provincial panel data in China from 2003 to 2019. The results show that: economic growth targets are positively correlated with carbon emissions under a series of endogeneity and robustness; there are regional heterogeneity, target heterogeneity and structural heterogeneity in the impact of economic growth targets on carbon emissions; after economic growth targets are set, government actions can influence carbon emissions by affecting resource mismatch and industrial restructuring; It is further found that there is a “U” shaped relationship between economic pressure and carbon emissions. Based on the above findings, this paper further proposes that a high-quality performance assessment mechanism should be developed to bring into play the active role of local governments in achieving carbon reduction goals, and thus contribute to high-quality economic development.

Wisual: Indoor Crowd Density Estimation and Distribution Visualization Using Wi-Fi

Driven by the Internet of Things (IoT), many device-free crowd density estimation techniques can roughly estimate the crowd density based on the relationship between the dynamic crowd and the variation of wireless signals. However, they cannot distinguish the path information of different persons in a fine-grained manner. In this article, we propose Wisual, a channel state information (CSI)-based device-free crowd density estimation framework and can visualize the distribution of people. The major challenge of Wisual is how to extract proper quantifiable indexes to distinguish the path information of multiple targets and maximize the resolution of crowd density estimation. To address this challenge, Wisual first presents a method for estimating the frequency of the CSI propagation path (FoC) for the moving persons and constructs a joint multifeature parameter (JMFP) spectrum matrix with the other two parameters. Then the multitarget spectrum matrix is put into a proposed deep-learning model called CSI stream 3-D convolutional neural networks (CS-3DCNNs) for implementing crowd density estimation and the target path information differentiation. Finally, Wi-Fi imaging is implemented based on the 2-D-MUSIC algorithm, which shows the approximate distribution situation of indoor persons through the spectrograms. The experimental results in typical real-world scenes demonstrate that Wisual can forecast the crowd density with 98% precision and accurately display the frequency spectra of moving persons. Besides, the results also prove the superior effectiveness, scalability, and generalizability of the proposed framework.

Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in multi-agent systems, e.g., pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances by centralized approaches with small computation time, and (2) online scenario: executing unlabeled-MAPF despite timing uncertainties of real robots. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm, which takes an arbitrary initial target assignment then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that Offline TSWAP is highly scalable; providing near-optimal solutions while reducing runtime by orders of magnitude compared to existing approaches. In addition, we present the benefits of Online TSWAP, such as delay tolerance, through real-robot demos.

Predictive Tracking Control for WMR Systems with Sensor and Control Delays

This paper addresses the control design problem for nonlinear Wheeled Mobile Robot (WMR) tracking with sensor and control delays. The tracking model is built by WMR dynamical equation. The state-space representations of the controlled WMR motion system and the dynamical target system are obtained by transformations. Then, the tracking error system with sensor and control delays is achieved and a nonlinear tracking control is presented. The physical implement problem of the predictive control is solved by constructing a high-gain predictor, based on which the predicted states in state feedback control are replaced by the predictive ones. The simulation results demonstrate that the physical implement problem are solved by designing a high-gain predictor. The target paths of a figure-of-8 and a circle are successfully tracked by the state feedback and output feedback controllers.

Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm.

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.

Dual Specific Phosphatase 7 Exacerbates Dilated Cardiomyopathy, Heart Failure, and Cardiac Death by Inactivating the ERK1/2 Signaling Pathway.

Heart failure is one of the most common but complicated end-stage syndromes in clinical practice. Dilated cardiomyopathy is a myocardial structural abnormality that is associated with heart failure. Dual-specificity phosphatases (DUSPs) are a group of protein phosphatases that regulate signaling pathways in numerous diseases; however, their physiological and pathological impact on cardiovascular disease remains unknown. In the present study, we generated two transgenic mouse models, a DUSP7 knockout and a cardiac-specific DUSP7 overexpressor. Mice overexpressing DUSP7 showed an exacerbated disease phenotype, including severe dilated cardiomyopathy, heart failure, and cardiac death. We further demonstrated that high levels of DUSP7 inhibited ERK1/2 phosphorylation and influenced downstream c-MYC, c-FOS, and c-JUN gene expression but did not affect upstream activators. Taken together, our study reveals a novel molecular mechanism for DUSP7 and provides a new therapeutic target and clinical path to alleviate dilated cardiomyopathy and improve cardiac function.

AUV path tracking with real-time obstacle avoidance via reinforcement learning under adaptive constraints

In this paper, the methods are proposed for underactuated autonomous underwater vehicle (AUV) to address three-dimensional (3D) path tracking and real-time obstacle avoidance. The errors of path tracking are generated based on the Serret–Frenet frame and line-of-sight (LOS) guidance law, while the errors in obstacle avoidance are obtained based on the carrier coordinate system to filter irrelevant environment information. On this basis, to deal with the complicated target path and unknown obstacles, the controller is designed by deep deterministic policy gradient (DDPG) algorithm and adaptive multi-constraints. The safety constraints are adopted in reward functions to avoid useless explorations and facilitate convergence. The training proceeds for path tracking and obstacle avoidance respectively. Compared to the original DDPG algorithm in the training, the proposed algorithm shows faster convergence. Various simulations are conducted under different initial conditions, and the results demonstrate the effectiveness of the proposed algorithm.

An emission inventory update for Tehran: The difference between air pollution and greenhouse gas source contributions

Many highly populated cities are still struggling to reach clean air targets, while the zero greenhouse gas emission objectives may accelerate the path toward healthy air for all. Still, there is a fine line between intensive electrification's impact on greenhouse gas emissions and criteria air contaminants depending on the source of the electricity. In this study, the previous version of the emission inventory for Tehran was evaluated and re-calculated in a detailed bottom-up approach to provide the most updated data on the contribution of stationary sources derived by power plants vs. mobile sources. The objectives were to update the emission inventory for improved policymaking, study the impact of changes in emissions in 4 years, provide a detailed methodology for cities highly impacted by transportation emissions, and exercise a simple yet effective modelling task for emission inventory evaluation. The study included all possible sources in the mobile sector with eight categories of fleet composition, industries, power plants, house heating, and point sources related to transportation such as terminals and gas stations. It also included exhaust, non-exhaust, and evaporative emissions for mobile sources. An intensive data collection campaign was launched to collect activity data. The traffic information was obtained from a travel-demand model and validated by traffic counting. The license plate registration database provided detailed fleet composition based on vehicle technology, fuel, age, and state of maintenance.The emission inventory calculated annual emissions of 478 kt for CO, 103 kt for NOx, 91 kt for VOCs, and 19 kt for SOx. TSP was estimated at 10.4 kt. NOx emissions were significantly increased from 87 to 102.6 ktonnes per year (17% increase) and were confirmed by an increase in the annual mean concentration of NO2 from 39.4 to 53.4 ppb (35% increase) between 2013 and 2017. A notable observation was the impact of house heating on CO2,eq emissions. More than 30% of CO2,eq emissions were from house heating (natural gas, predominantly methane) in a city where total air contaminant emissions are highly driven by the transportation sector. The result clearly shows the differences between zero-emission target paths for the two objectives of greenhouse gas emissions and air quality. An integrated approach is needed to develop policies that will lead o zero-emission GHG targets while keeping the air clean.

Optimization of Regional Logistics Distribution Information Network Platform

In order to solve the problem of regional logistics distribution, a study on the optimization of regional logistics distribution information network platform is proposed. Firstly, in order to meet the development requirements of regional logistics and improve customer satisfaction, the regional logistics path is optimized to solve the problem of long time consumption for the optimization algorithm of regional logistics distribution path to find the optimal path. The clustering algorithm is used to divide the logistics distribution area, and the multiobjective function of logistics distribution path optimization is established, mainly including weight index, timeliness index, customer importance index, time window index, and total path index. The distribution target weight is set to find the best target path, so as to realize the optimization of regional logistics distribution path. The experimental results show that the time for the designed optimization algorithm to find the optimal path is less than 2 minutes, while the time for the traditional algorithm to find the optimal path is more than 3 minutes, up to 4.6 minutes. The comparison shows that the traditional algorithm takes more time to find the optimal path in five times than the algorithm designed this time. Therefore, the optimization method of this design takes less time to find the optimal path than the traditional method, which proves the effectiveness of the regional logistics distribution path optimization algorithm.

Path Optimization of Agricultural Robot Based on Immune Ant Colony: B-Spline Interpolation Algorithm

This study presents a path planning algorithm based on immune ant colony B spline interpolation to eliminate path duplication and corner inflection points, increase the smoothness of the agricultural robot’s path trajectory, and enhance the path planning performance. Firstly, this study improves the convergence speed of the algorithm by introducing an adaptive factor that obeys the normal distribution to optimize the path heuristic function. Secondly, to strengthen the variety of the target path search, an immune algorithm was utilized to perform a global investigation of the appropriate routes and assign the initial pheromones of the ant colony. Then, the adaptive judgment factor is introduced based on the incentive degree function and the pheromone evaporation coefficient, and the update formula are adjusted to promote the algorithm’s flexibility while suppressing the optimal regional problem. Finally, the pathways are processed using a cubic B spline interpolation method to remove the edges and minimize the peak inflection points. To verify the specific optimization performance of the algorithm in this paper, we have selected the same type of improved algorithm as a comparison and tested it in a raster map model. The simulation results demonstrate that the values of the proposed algorithm paths and the standard deviation of the tracks are smaller than the comparison algorithm, which indicates the higher performance of the algorithm in searching the optimal routes and more flexible and accurate search. Compared with the comparison algorithm, the average time-consuming reduction rate of the algorithm in this paper is between 38% and 45% in different grid maps, and the number of turns is reduced by 33% to 58%. It meets the requirements of the actual work on the robot.

Path tracking of autonomous vehicle based on adaptive preview trajectory planning with the consideration of vehicle stability

The path control gives the target path through planning, and uses the tracking strategy to make the vehicle converge to the target path. How to balance the tracking performance and the vehicle stability is a crucial and worthy research for the autonomous vehicle safety. In this paper, a hierarchical path control strategy consists of path planning and tracking with the consideration of vehicle lateral stability is proposed. In the adaptive preview distance block, the preview distance is adaptively regulated according to the vehicle speed, sideslip angle, and the preview trajectory curvature to balance the tracking error and stability. In the path planning block, the three-order polynomial fitting method is adopted to give the desired path according to the preview distance and the relative position relationship between vehicle and road or obstacles. The linear quadratic regulator (LQR) controller is designed to tracking the desired path fully using the previewed curvatures and the vehicle motion error. The hardware in the loop (HIL) simulation and vehicle test results illustrate that the proposed strategy can deal with path tracking, avoidance and lance change scenes in low computation burden, and maintain the stability of vehicle simultaneously.

Three-dimensional pose detection method based on keypoints detection network for tomato bunch

Non-destructive picking of fresh tomatoes is a delicate agronomical operation, based on comprehensive information about the plant organ, such as the location of stem, peduncle, and fruits. The matching between visual information supply and information demand from the agronomical technic is the key power to promote the picking robot from the laboratory to the field. The three-dimensional pose information, containing the location of each organ of the plant, can meet the demand of agronomical technic. It is the premise of precisely handling the cluster of fruits. In order to realize the fine tomato bunch harvesting operation in a bunch, this paper proposed a three-dimensional pose detection method for tomato bunch. The method, named Tomato Pose Method (TPM), is composed of a priori geometric model, a cascaded multi-task network, and a three-dimensional reconstruction process. Based on prior knowledge and agronomic technology, this prior geometric model comprehensively and flexibly describes the spatial location information of tomato bunch. The cascaded multi-task network is designed based on hourglass structure and transfer learning, which is suitable for bounding box and key point prediction of tomato bunches in complex environments. Finally, combining the prior geometric model and the spatial position information of each key point, the tomato bunch is reconstructed. Only a medium training dataset, containing 1800 RGBD images covering changing lighting, occlusion, and various poses, is needed for training. Its success rate of TPM on two-dimensional keypoint detection is 94.02%, the accuracy of 85.77% predicted points are at medium level. And 70.05% tomato bunch with multi-pose can be constructed. More importantly, this method only needs one RGBD image taken by a commercial camera to realize the three-dimensional reconstruction of a single-bunch scenario in 1.0 s, and a multi-bunch scenario in 2.0 s. It provides comprehensive information, and provides data basis for target positioning and path planning of picking robot, which makes the non-destructive harvesting possible.

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners.

The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 m error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

How to Assess Sustainable Planning Processes of Buildings? A Maturity Assessment Model Approach for Designers

Over the past decades, it has become apparent that increasing demands in the construction industry have repeatedly led to project delays and increased project costs in practice. These demands have increased as a result of international and national action plans that have been developed to achieve the climate target paths and, therefore, the necessary reduction of CO2 emissions in the construction industry. We address this problem by developing a sustainable construction maturity model (SCOMM) to answer the following research question: “What is a holistic quality assurance tool for the early design phase of buildings to monitor (sustainable) planning practices in order to achieve better certification results?”. The model includes a self-assessment procedure for the building design process, based on Software Process Improvement and Capability dEtermination (SPiCE) and the German Sustainable Building Council (DGNB) building certification system. The results show that systemic interactions between sustainability criteria can be identified in the early design phase, allowing the quality of planning practices to be evaluated and early project management to be implemented to achieve the best certification results. Our findings will enable clients and users of the construction industry to better manage the complexity of the sustainable design process and avoid undesirable developments in building projects.

Research on power-supply cost of regional power system under carbon-peak target

With the establishment of the carbon-peak target by 2030, the direction of carbon emission reduction in China’s energy system has been further clarified. As the industry with the largest proportion of carbon emissions in China, the low- carbon transformation of the electric power industry is critical to realize the carbon-peak target. Current research mostly focuses on technical analysis or system cost accounting of the carbon-peak realization path at the national level. There is a lack of targeted research on regional power systems with complex inter-regional power flow exchange and limited energy resource development. Simultaneously, the calculation of the system cost lacks the perspective of the life cycle and ignores the inertia of the stock and change inertia of incremental disturbance. From the perspective of the life cycle, this study proposes a calculation model of power supply cost for regional power systems according to the carbon-peak target, analyzes the realization path of the carbon target from an economic perspective, and provides references for the path selection and policy formulation of system transformation

Comparison of visual servoing technologies for robotized aerospace structural assembly and inspection

This work presents a novel approach for visual servoing of robotized aerospace manufacturing cells, based on the combined use of a camera and a 2D-beam scanner and a 1-D beam distance sensor attached to the end-effector of a collaborative robot. The proposed system can detect features associated with mechanical bounds over the aircraft structure, making possible the robot automatic online trajectory/path generation when the robot performs a target task over an aeronautical part. The effectiveness of this method is demonstrated by means of experimental evaluations carried out in unstructured environments without illumination and temperature control (simulating real shop floor conditions), evincing that the proposed approach is more robust. We also show that it is able to automatically generate and follow a target path with an accuracy of 0.40 mm and repeatability of 0.59 mm, which is roughly 2 times more accurate than the classical computer vision servoing used in the experiments. The proposed solution is suitable to applications in modern collaborative robotized aerospace assembly cells.

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motor-driven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim®, shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.

Reflections on the Teaching Reform of Art Theory Courses in Colleges and Universities from the Perspective of Aesthetic Education Practice

Under the background of the new era of aesthetic education, the internal and external environment of art theory courses in our country’s colleges and universities has undergone fundamental changes. The national new liberal arts education, curriculum ideological and political co-education, promotion of excellent Chinese traditional culture and other educational strategic goals, and the rapid development of the Internet and artificial intelligence have become the fundamental influencing factors of the changes in the external environment. The composition of its internal environment is still the contradictory relationship and status quo between teachers, students, textbooks, teaching methods, and evaluation methods that have been formed for a long time in traditional classroom teaching. Therefore, when reforming the strategic thinking of the traditional teaching model, we must pay attention to the internal requirements of the new liberal arts education and curriculum ideology and make full use of the Internet and artificial intelligence technology platform. We must take aesthetic education practice as the target path, strengthen the construction of art theory teachers, optimize the art theory curriculum system, reform the traditional classroom teaching methods and evaluation system, and cultivate a new generation of people with humanistic spirit and innovative thinking that meet the needs of the development of the times.