Earticle

In recent years the USN(Ubiquitous Sensor Networks) technology has been applied in the greenhouse in order to control temperature and humidity automatically. In this paper, we proposed a control algorithm using feedback linearization techniques based on a mathematical model for temperature and humidity environment. Especially, Control algorithm is presented to the operation of the ventilator affecting on the temperature and humidity system at the same time. The System has been designed taking into account the disturbance(External temperature, soil temperature, external humidity, solar radiation and wind). In conclusion, I will present a way to control multiple actuator through simulations. The proposed control algorithm is validated using the Matlab / Simulink tools.

Automatic guided vehicle in the factory has an important role to advance the flexible manufacturing system. In this paper, we propose a novel object-transportation control algorithm of cooperative AGV systems to apply decentralized control to multiple AGV systems. Each AGV system is under nonholonomic constraints and conveys a common object-transportation in a horizontal plain. Moreover it is shown that cooperative robot systems ensure stability and the velocities of augmented systems convergence to a scaled multiple of each desired velocity field for cooperative AGV systems. Finally, the application of proposed virtual passivity-based decentralized control algorithm via system augmentation is applied to trace a circle. Finally, the simulation and experimental results for the object-transportation by two AGV systems illustrates the validity of the proposed virtual-passivity decentralized control algorithm.

A new generation of versatile video coding (VVC) standards was released in 2020; however, existing rate control algorithms for three-dimensional (3D) video coding based on VVC do not consider the effects of scene switching in actual video applications. Under the background of scene switching, the quality of encoded images degrades owing to the unreasonable allocations of coding resources. To solve this problem, this study proposes a 3D video basic view rate control algorithm based on scene switching detection of image structure similarity. First, scene switching is detected before coding. Basic view target bits estimated by the allocation model are first estimated by view weight factors. Finally, scene switching is judged by combining the structural similarity between frames with its transformation trend. When scene switching occurs, the rate control parameters and coding structure are adjusted in time. Experimental results show that the proposed algorithm not only tracks the target bit rate accurately but also significantly improves the reconstructed image quality in the case of scene switching and ensures that the decoder input buffer does not overflow or underflow.

The intelligent reflecting surface (IRS) technology can enhance coverage in wireless networks by detouring a blocked path, where the phases of its passive elements are controlled to reinforce reflected signals in the desired directions, as the massive MIMO does. As a massive IRS system is introduced in the millimeter wave (mmWave) and tera-hertz (THz) bands, the optimal phase control induces computational complexity, and its complexity is doubled in multiuser systems. We propose a computationally efficient phase control algorithm in IRS-aided multiuser systems, where the reflected signals are sequentially aligned to search for optimal phases to maximize the total SNR of all users in service. The proposed scheme is evaluated with continuous phase and discrete phases with 1, 2, and 3 bits and compared with the random phase and full-search cases.

In wearable healthcare systems, sensor devices can be deployed in places around the human body such as the stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient. A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop. If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy consumption more than previous systems that only consider the sensor part. In this paper, we propose a new approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy management of sensor devices. Our proposed approach judges the current channel condition based on the values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL without communication to save the sensor's batteries. Otherwise, they find an optimal TPL. To compare performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as linear, binary, hybrid, and ATPC.

The increasing demand for high-speed, low-latency applications, especially with 5G mmWave technology, has led to challenges in TCP performance due to signal blockages, small buffers, and high Packet Error Rates (PERs). Existing congestion control algorithms (CCAs) struggle to fully utilize available bandwidth under these conditions. This paper proposes MSS-TCP, a novel congestion control algorithm designed for mmWave networks. MSS-TCP dynamically adjusts the congestion window (cwnd) based on the maximum segment size (MSS) and round-trip time (RTT), improving bandwidth utilization and congestion adaptability. The simulation results using the ns-3 network simulator show that MSS-TCP outperforms state-of-the-art CCAs, including NewReno, HighSpeed, CUBIC, and Bottleneck Bandwidth and Round-trip propagation time (BBR), and Fuzzy Logic-based (FB-TCP), particularly when the buffer matches the bandwidth-delay product (BDP), achieving a 24.26% to 45.43% improvement in throughput compared to BBR while maintaining low latency. These findings demonstrate that MSS-TCP enhances TCP performance in 5G mmWave networks, making it a promising solution for next-generation wireless communication.

The congestion control algorithm (CCA) of Multipath TCP (MPTCP) mainly portraits two fundamental challenges to properly utilize the available bandwidth. First, MPTCP should provide an incentive over single-path TCP while not harming any existing single-path TCP flows when sharing the same bottleneck. Second, better utilize the paths by ensuring high throughput with low packet losses. We focus to improve the second goal by implementing a novel technique to dynamically control the decrease factor of the CCA depending on the interval between packet losses. We implemented in Linux kernel and found a significant performance improvement by extensive the Mininet experiments.

Windshield wipers play a key role in assuring the driver's safety during precipitation. The traditional wiper systems, however, requires driver's constant attention in adjusting the wiper speed and the intermittent wiper interval because the amount of precipitation on the windshield constantly varies according to time and vehicle's speed. Because the manual adjustment of the wiper distracts driver's attention, which may be a direct cause of traffic accidents, many companies have developed automatic wiper systems using some optical sensors with various levels of success. This paper presents the development of vision-based smart windshield wiper system that can automatically adjust its speed and intermittent interval according to the amount of water drops on the windshield. The system employs various image processing algorithms to detect water drops and fuzzy logic to determine the speed and the interval of the wiper.

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

The paper describes experimental work that demonstrates the use of repetitive control to attenuate radial vibrations of a variable speed-rotor. The experiments were performed on a rotor test rig having a 3-kg rotor supported by journal bearings. The first bending resonance of the rotor shaft (i.e. the critical speed) was approximately 50 Hz. The objective was to control the radial response at the rotor midpoint by using an actuator located outside the bearing span. A novel aspect of the controller design is that the length ofthe control output vector of the repetitive controller was updated as a function of the speed of rotation. The speed of rotation determined the required delay time and the repetitive filter length that approximately matches with the delay time. The results obtained were comparable to those achieved in earlier studies with feedforward compensation methods. The best results were achieved when the frequency of rotation enables an integer ratio between disturbance period and sample rate.

In this paper we propose a Differentiated Services Based Admission Control and Routing Algorithm for IPv6 (ACMRA). The basic DiffServ architecture lacks an admission control mechanism, the injection of more QoS sensitive traffic into the network can cause congestion at the core of the network. Our Differentiated Services Based Admission Control and Routing Algorithm for IPv6 combines the admission control phase with the route finding phase, and our routing protocol has been designed in a way to work alongside DiffServ based networks. The Differentiated Services Based Admission Control and Routing Algorithm for IPv6 constructs label switched paths in order to provide rigorous QoS provisioning. We have conducted extensive simulations to validate the effectiveness and efficiency of our proposed admission control and routing algorithm. Simulation Results show that the Differentiated Services Based Admission Control and Routing Algorithm for IPv6 provides an excellent packet delivery ratio, reduces the control packets' overhead, and makes use of the resources present on multiple paths to the destination network, while almost each admitted flow shows compliance with its Service Level Agreement.

A robust optimal control design is proposed in this study for rigid robotic systems under the unknown loads and the other uncertainties. The uncertainties are reflected in the performance index, where the uncertainties are bounded for the quadratic square of the states with a positive definite weighting matrix. An iterative algorithm is presented for the determination of the weighting matrix required for necessary robustness. Computer simulations have been done for a weight-lifting operation of a two-link manipulator and the simulation results shows that the proposed algorithm is very effective for a robust control of robotic systems.

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

본 논문에서는 효율적인 제어를 위하여 캡슐 앤도스코피에 대한 모델링을 실시하였다. 방법론적으로 루그레 모델에 대한 시스템 특성파악을 위한 수학적 모델링을 이용하였다. 비선형 마찰 모델인 루그레 모델에 바탕을 둔 stick-slip 모션 시스템이 인체내에서의 캡슐 앤도스코피의 활동을 묘사하는 시뮬레이션 모델로 이용하였다. 다양한 상황을 고려하여 루그레 마찰모델에 대한 시뮬레이션을 Matlab Simulink 를 작성하여 수행하였다. 전체적인 모션과 파라미터의 영향이 엔도스코피의 속도에 미치는 영향에 대한 것에 주안을 두어 실시하였다.

In this paper, basic backgrounds about capsule endoscopy are introduced, and the aims and objectives are also illustrated. Methodology and mathematical model for LuGre model were investigated to analyse system characteristics. A nonlinear friction model, the stick-slip motion system based on LuGre friction model was used to simulate the motion of capsule endoscopy inside human body. Under the different situation, LuGre friction model was simulated by Matlab Simulink software. The entire cycle of motion and the influence of parameters towards to velocity are fully simulated.

IoT 서비스는 경량 디바이스 발전으로 그 범위가 계속 확대되고 있으며, 이에 따라 네트워크의 혼잡성은 크게 증가하고 있다. IoT 네트워크 환경에서 CoAP를 위한 기존 혼잡 제어 메커니즘은 동적 네트워크 조건에 대한 적응력이 부족하다. 본 연구에서는 IoT 서비스 환경의 네트워크 환경의 네트워크 혼잡 문제를 해결하기 위해 적응 형 신경망 기반 혼잡 제어(ANN-CoCC) 알고리즘을 제안한다. 정적 매개변수나 휴리스틱 규칙에 의존하는 기존의 혼잡 제어 방식과 달리 ANN-CoCC는 머신러닝 모델을 적용하여 실시간 네트워크 상태에 따라 재전송 시간 초과 (RTO), RTT 분산, 혼잡 윈도우와 같은 주요 전송 매개변수를 동적으로 최적화한다. 다양한 패킷 손실률과 노드 밀도에서 실험 평가를 수행한 결과, 본 연구에서 제안하는 알고리즘은 CoCoA+, BDP-CoAP, pCoCoA과 같은 기존 방식과 비교했을 때 처리량을 30% 이상 향상시키며, 지연 시간 감소, 패킷 손실을 최소화하는 것으로 확인되 었다.

The scope of IoT services continues to expand with the development of lightweight devices, and network congestion is increasing significantly. In this study, we propose an adaptive neural network-based congestion control (ANN-CoCC) algorithm to solve the network congestion problem in the network environment of IoT service environments. Unlike existing congestion control methods that rely on static parameters or heuristic rules, ANN-CoCC applies a machine learning model to dynamically optimize key transmission parameters such as retransmission timeout (RTO), RTT distribution, and congestion window according to real-time network conditions. The experimental evaluation results under various packet loss rates and node densities show that the proposed algorithm improves throughput by more than 30%, reduces delay, and minimizes packet loss compared to existing methods such as CoCoA+, BDP-CoAP, and pCoCoA.

This paper proposes a methodology for the small-displacement-movement of a piston and develops a hybrid control algorithm for the precision position control of a pneumatic rodless cylinder. The pneumatic system uses the voltage-proportional solenoid valves to minimum valve switching since the on/off type valves are create diffculties for accurate position control and induce a lot of valve switching. For the accurate position control a methodology for the small-displacement-movement of the piston is developed and identified experimentally. The main consideration on the development of the hybrid control law is to eliminate a stick-slip phenomenon in the pneumatic control system. This paper addresses these critical issues and presents experimental results for the pneumatic control system.

본 논문에서는 기존에 연구되었던 Thayer의 인간의 감성 모델을 바탕으로 음악을 듣고 느끼는 감성을 8가지 감성으로 정의한 음악 감성모델로 제시하였다. 감성에 영향을 주는 음악의 요소는 음악의 템포, 역동성, 진폭변화, 밝기, 잡음 등 5가지로 선택하였다. 8가지 감성으로 이루어진 감성 모델에 따라서 8가지 감성으로 분류된 160곡의 노래를 선곡하여 실제 데이터를 추출하여 분석하였다. 실제 데이터의 분석을 통해 5가지 요소의 가중치로 이루어진 감성 수식을 도출하였고 임의의 음악에 대하여 감성 수식을 통해서 2차원 감성 좌표계에 매핑 하여 감성을 예측할 수 있도록 알고리즘을 설계하였다. 또한 2차원 감성 좌표계에서의 좌표 값을 이동시켜 감성을 제어할 수 있는 방법을 제시하였다.

This thesis proposes the emotions acquired after listening to the music as an emotion model composed of 8 types of emotions, based on the emotion model studied previously. The 5 musical factors selected, that affect the emotion, are tempo, dynamics, amplitude change, brightness, and noise. According to the emotion model composed of 8 types of emotions, 160 songs categorized into the 8 types of emotions were selected, and the actual data was extracted and analyzed. Through the analysis of actual data, an emotion equation made of weighted value of 5 factors was derived, and an algorithm that can predict the emotion by mapping on the 2-dimensional emotion coordinate system through the emotion equation was designed. Also, a way of controlling emotion by moving the coordinates on the 2-dimensional emotion coordinate system was suggested.

As the technical advances in portable computers and wireless communication technologies, mobile computing environment has been rapidly expanded. The mobile users on mobile host can access information via wireless communication from the distributed heterogeneous multidatabase system in which pre-existing independent local information systems are integrated into one logical system to support mobile applications. Hence, mobile transaction model should include not only the features for heterogeneous multidatabase systems but also the ones for mobile computing environment. In this paper, we proposed a mobile flex transaction model which extends the flexible transaction model that previously proposed for heterogeneous multidatabase systems is extended to support the requirements of mobile heterogeneous multidatabase systems. We also presented the execution control mechanism of the mobile flex transaction model. The proposed mobile flex transaction model allows the definition of location-dependent subtransactions, the effective support of hand-over, and the flexibility of transaction executions. Hence, the proposed mobile flex transaction model can be suit to mobile heterogeneous multidatabase systems that have low power capability, low bandwidth, and high communication failure possibility.

A study for a lathe to machine workpiece with noncircular cross-section is presented. The noncircular cutting is accomplished by controlling radial tool position synchronized with revolution angle of the spindle according to the desired cross-sectional shape. A learning control algorithm is suggested for the tool positioning. The learning law of the algorithm is based on pole-zero cancellation, which guarantees the control stability. The control performances are analyzed and simulated on a numerical computer that the effectiveness of the control algorithm is convinced. The algorithm is tested on a conventional NC-lathe which shows some successful results.