石油化学コンビナート火災対応消火ロボットの自律性に関する研究

Tohoku University田所諭課

A Hybrid Controller with Chedoke-McMaster Stroke Assessment for Robot-Assisted Rehabilitation

AbstractAmongst the major challenges in post-stroke rehabilitation are the repetitiveness nature of rehabilitation procedure, and the accessibility of therapists for long-term treatment. In manual rehabilitation procedure, the patient is subjected to repetitive mechanical movement of the affected limb by the therapist. In one of the techniques called active-assist exercise, the subject moves his affected limb along a specified trajectory with the therapist guiding the motion. The therapist gives some assistance to the subject to complete the course if deemed necessary and the procedure repeats. The significant advantages of using robots in assisting rehabilitation are its efficiency and it is fatigue free. The robots however need to be developed to have the capability of human therapist in providing the rehabilitation more naturally. In this paper, the work focuses on developing a new framework for the robot controller system. In particular, a low-level controller, which is in the form of force controller based on impedance control theory is discussed. The controller is capable of governing the active-assist exercise through autonomous guidance during the therapeutic procedure based on the Chedoke-McMaster stroke assessment method

Surface Electromyography (sEMG)-based Thumb-tip Angle and Force Estimation Using Artificial Neural Network for Prosthetic Thumb

AbstractNormally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. In this work the relationship between the electromyogram (EMG) signals and thumb tip forces are investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The classification of the EMG signals based on different force and thumb configuration is performed by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to classify the EMG signals

Air Filter Dust Level Sensing System Using Fuzzy Logic

The conventional air filter dust level checking process needs to manually inspect an air conditioner. This process is dangerous for users because the installation of the air conditioner is high. Hence, this study demonstrates a proposed air filter dust level sensing system that can automatically inspect the dust level inspection and reduce the relying on human visual inspection which is subjectively and tedious. The proposed system employs Ohm’s law for sensing the current of the fan motor of an air conditioner, and employs fuzzy logic controller to estimate the dust level. The current of the fan motor would be processed to determine the relationship between the air filter blockage conditions. The finding shows that the current was proportional to the air filter blockage condition. The lowest current value was 0.2742A for no air filter blockage whereas the highest current value was 0.2898A for full air filter blockage. The system contributes to the society for having a better life as the system is promising to be adapted to inform the users about the air filter dust level of their air conditional

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

Effect of storage temperature and duration on physico-chemical properties, microbial growth and nutritional composition of papaya and banana fruits

Kultivar pisang (Musa sp.) dan betik (Carica papaya) dituai dan disimpan selama sebulan pada suhu penyimpanan dan jangka masa berbeza. Buah-buahan ini diambil daripada kebun yang terletak di Johor Bahru, Malaysia. Masak dalam tempoh yang tidak diingini, perubahan warna kulit, penurunan berat, buah-buahan kehilangan kualiti seperti nutrisi, kadar serangan mikroorganisma tinggi, dan kerosakan buah-buahan kerana cara penyimpanan tidak wajar. Buah-buahan itu disimpan pada jangka masa berbeza (0, 3, 6, 14, 30, hari) dan pelbagai suhu penyimpanan (4 ±1, 10±2, 30±3 °C). Perubahan warna, kehilangan berat, jumlah polisakarida dan protein, jumlah gula larut (TSS), keasidan boleh titrat (TA), pH, kandungan bebas fenol (FPC) dan pertumbuhan mikrob ditentukan. Keputusan menunjukkan parameter tersebut dipengaruhi dengan ketara oleh suhu dan masa. Contohnya, polisakarida pisang adalah 20 mg/L pada 4°C, 20 mg/L pada 10°C, dan 16 mg/L pada 30°C, protein pisang adalah 1155 pada 4°C, 1315 pada 10°C, dan 1640 pada 30°C, jumlah gula larut adalah 6.8 pada 4°C, 7.9 pada 10°C, dan 8.2 pada 30°C, pH pisang adalah 4.8 pada 4°C, 4.8 pada 10°C, dan 5.9 pada 30°C, kandungan bebas fenol betik (FPC) adalah 184 pada 4°C, 245 pada 10°C dan 569 mg/L pada 30°C, dan kehilangan berat betik adalah 7 pada 4°C, 15 pada 10°C dan 65% pada 30°C. Warna betik tidak berubah pada jangka masa penyimpanan yang panjang penyimpanan sejuk. Di samping itu, warna kulit pisang terjejas akibat pemerangan dan kecederaan pendinginan ke dalam penyimpanan sejuk. Selain itu, semasa penyimpanan, jumlah gula larut, (TSS), jumlah polisakarida, keasidan boleh titrat (TA), dan kandungan bebas fenol (FPC) meningkat semasa penyimpanan sejuk. Tambahan lagi, pH, pertumbuhan mikrob, berat, dan anggaran protein pula meningkat semasa penyimpanan sejuk. Kesimpulannya, berdasarkan keputusan yang diperoleh, suhu dan jangka masa penyimpanan optimum untuk pisang didapati pada 4°C dan 14 hari dan sebulan untuk buah betik. Dapatan keseluruhan kajian ini boleh menyediakan alat pengurusan berasaskan sains untuk prestasi penyimpanan buah pisang dan betik

Benefits Optimization through Ill-Informed Client Competency Acquisition and Engagement in Renovation Works Flow

In Malaysia, landed residential building design for mass housing has been influenced by the orientation towards a “seller’s market, without prioritizing the changing needs of the owner-occupant. This has contributed to the growing trend of having to “remodel” homes that is currently dominated by “low-value adding practices” that are embedded within traditional benefits realization principles, amounting to brief freezing. There is a disregard for client’s engagement at the construction phase, wherein the client is constrained by the practice of restrictive benefits realizations. This issue is underlined by a predominant positivist orientation to the issue of client participation that does not recognize residential housing client’s ability for competency acquisition in realigning requirements to maximize benefits. This paper proposes that value maximization for such a client can best be achieved through dynamic engagement with the renovation contractor to allow for value-driven ‘disruptive innovation’ practice during the construction phase. Focusing on requirements capture as a process rather than an output, it is proposed that client’s requirements can be realigned to maximize benefits based on a dynamic benefits realization model. This issue of benefits maximization is viewed from a social science perspective of primary stakeholder engagement within a legitimate peripheral mode of participation acting from within a community of practice whilst operating in a relational contracting environment. DOI: 10.5901/mjss.2016.v7n5p33

Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Nowadays, the advancement of drones is also factored in the development of a world surrounded by technologies. One of the aspects emphasized here is the difficulty of controlling the drone, and the system developed is still under full control by the users as well. Reinforcement Learning is used to enable the system to operate automatically, thus drone will learn the next movement based on the interaction between the agent and the environment. Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. A comparison of both Q-learning and State-ActionReward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller

Development of a dodecacopter using Pixhawk 2.4.8 autopilot flight controller

This research focused on the development of unmanned aerial vehicle (UAV) of a dodecacopter system. The dodecacopter is controlled through a Pixhawk 2.4.8 firmware�based flight controller. The communication between the dodecacopter and flight controller is connected through wireless communication system. The dodecacopter is well equipped with latest technology from Pixhawk flight controller for efficient and smooth controlling system. The dodecacopter balancing system is using the technology of barometer MS5611 and magnetometer IST8310 sensors. The developed dodecacopter is equipped with 12 Tarot 4006 brushless motors to increase the payload capability. Based on the results, the experiment shows that the dodecacopter can hover and maintain its position with optimum stability. Observation shows that the dodecacopter performances can be increased by using high-powered motors, lighter batteries

Design of water level detection monitoring system using fusion sensor based on Internet of Things (IoT)

River flooding is a condition when the water in a river overflows and exceeds its normal capacity, thereby flooding the surrounding area. This flood disaster has been a known problem for a long time and causes great damage in the affected areas. Flood events in Rivers are influenced by many factors, such as climate change, rapid urbanization, inappropriate land use, ineffective water management patterns, as well as uncontrolled addition of hard soil surfaces. Flood conditions in rivers involve complex processes and are influenced by various factors components, such as rainfall, water flow, topography, vegetation, and many other factors. Therefore, this research is very urgent because it can help reduce the negative impacts of flooding, increase public safety, become a basis for decision making, save costs and resources and make a positive contribution to technological development. This study aims to create a prototype of a flood early warning system. The system is based on a wireless sensor network whose interconnections are connected by a star topology. Every node is a combination of several sensors (sensor fusion) that are related to detecting floods, such as: height sensors, water flow speed sensors and rainfall intensity sensors. Design of hardware (hardware) and software (software) will be done. A classification mechanism based on Fuzzy Logic will be used to estimate flood conditions based on existing data. Flood estimation will determine the time and distance of flood events that will occur. Several experiments in the laboratory will be carried out to determine the performance of the designed system